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Pollen-based biome reconstructions for Latin America at 0, 6000 and 18 000 radiocarbonyears

Marchant, R.; Harrison, S.P.; Hooghiemstra, H.; Markgraf, V.; van Boxel, J.H.; Ager, T.;Almeida, L.; Anderson, R.; Baied, C.; Behling, H.; Berrio, J.C.; Burbridge, R.; Björck, S.;Byrne, R.; Bush, M.B.; Cleef, A.M.; Duivenvoorden, J.F.; Flenley, J.R.; de Oliveira, P.; vanGeel, B.; Graf, K.J.; Gosling, W.D.; Harbele, S.; van der Hammen, T.; Hansen, B.C.S.; Horn,S.P.; Islebe, G.A.; Kuhry, P.; Ledru, M-P.; Mayle, F.E.; Leyden, B.W.; Lozano-García, S.;Melief, A.B.M.; Moreno, P.; Moar, N.T.; Prieto, A.; van Reenen, G.B.; Salgado-Labouriau,M.L.; Schäbitz, F.; Schreve-Brinkman, E.J.; Wille, M.Published in:Climate of the Past Discussions

DOI:10.5194/cpd-5-369-2009

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Citation for published version (APA):Marchant, R., Harrison, S. P., Hooghiemstra, H., Markgraf, V., van Boxel, J. H., Ager, T., ... Wille, M. (2009).Pollen-based biome reconstructions for Latin America at 0, 6000 and 18 000 radiocarbon years. Climate of thePast Discussions, 5(1), 369-461. https://doi.org/10.5194/cpd-5-369-2009

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Clim. Past Discuss., 5, 369–461, 2009www.clim-past-discuss.net/5/369/2009/© Author(s) 2009. This work is distributed underthe Creative Commons Attribution 3.0 License.

Climateof the Past

Discussions

Climate of the Past Discussions is the access reviewed discussion forum of Climate of the Past

Pollen-based biome reconstructions forLatin America at 0, 6000 and18 000 radiocarbon yearsR. Marchant1, S. P. Harrison2, H. Hooghiemstra3, V. Markgraf4, J. H. van Boxel3,T. Ager5, L. Almeida6, R. Anderson7, C. Baied8, H. Behling9, J. C. Berrio10,R. Burbridge11, S. Bjorck12, R. Byrne13, M. B. Bush14, A. M. Cleef3,J. F. Duivenvoorden3, J. R. Flenley15, P. De Oliveira16, B. van Geel3, K. J. Graf17,W. D. Gosling18, S. Harbele19, T. van der Hammen3,20, B. C. S. Hansen21,S. P. Horn22, G. A. Islebe23, P. Kuhry24, M.-P. Ledru25, F. E. Mayle26,B. W. Leyden32, S. Lozano-Garcıa27, A. B. M. Melief3, P. Moreno28, N. T. Moar29,A. Prieto30, G. B. van Reenen3, M. L. Salgado-Labouriau31, F. Schabitz33,E. J. Schreve-Brinkman3, and M. Wille33

1The York Institute for Tropical Ecosystem Dynamics (KITE), Environment Department,University of York, York, Heslington, YO10 5DD, UK2Bristol Research Initiative for the Dynamic Global Environment (BRIDGE), School ofGeographical Sciences, University Road, University of Bristol, Bristol BS8 1SS, UK3Institute for Biodiversity and Ecosystem Dynamics (IBED), Faculty of Science, University ofAmsterdam, Postbus 94062, 1090 GB Amsterdam, The Netherlands

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4 INSTAAR, University of Colorado, Boulder, Colorado 80309, USA5 USGS, National Centre, MS 970, Reston, Virginia 22092, USA6 Laboratorio Biogeografıa, Facultad de Ciencias, Universidad Nacional Autonoma de Mexico,Aptdo Postal 70-296, 04510 Mexico D.F., Mexico7 Department of Geography, University of Montana, Missoula, Montana 59812-1018, USA8 Environmental Studies Program, University of Montana, Missoula Montana 59812, USA9 Georg-August-Universitat, Albrecht-von-Haller-Institut fur Pflanzenwissenschaften, AbteilungPalynologie und Klimadynamik, Untere Karspule 2, 37073 Gottingen, Germany10 Department of Geography, University Road, University of Leicester, LE1 7RH, UK11 c/o. Geography Building, Drummond Street, Edinburgh, EH8 9XP, UK12 Geological Institute, Univ. of Copenhagen, Øster Volgade 10, 1350 Copenhagen, Denmark13 Department of Geography, University of California, Berkeley, California 94720-4740, USA14 Department of Biological Sciences, Florida Institute of Technology, 150 W. UniversityBoulevard, Melbourne, Florida 32905, USA15 Department of Geography, Massey University, Palmerston, New Zealand16 Instituto de Geociencias-DPE, Universidade de Sao Paulo, Caixa Postal 11348, Sao Paulo,SP 05422-970, Brazil17 Geographisches Institut der Universitat, Winterthurerstrae 190, 8057 Zurich, Switzerland18 Department of Earth and Environmental Sciences, CEPSA, The Open University, WaltonHall, Milton Keynes, MK7 6AA, UK19 Dept. of Geography and Environmental Science, Monash Univ., Clayton, Victoria, Australia20 Fundacion Tropenbos Colombia, Carrera 21 #39-35, Santafe de Bogota, Colombia21 Limnological Research Centre, University of Minnesota, 220 Pillsbury Hall, 310 PillsburyDrive, Minneapolis, Minneapolis 55455-0219, USA22 Department of Geography, University of Tennessee, 408 G&G Building, Knoxville,Tennessee 37996-1420, USA

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23 El Colegio de la Frontera Sur. ECOSUR-Chetumal, Apartado Postal 424, Chetumal,Quintana Roo, CP 77000, Mexico24 Department of Physical Geography and Quaternary Geology, Stockholm University,10691 Stockholm, Sweden25 Equipe Paleoenvironnements, Institut des Sciences de l’Evolution Institut de Recherchepour le Developement, Montpellier, France26 Geography Building, Drummond Street, Edinburgh, EH8 9XP, UK27 Universidad Nacional Autonoma de Mexico, Instituto de Geologıa, Aptdo Postal 70-296,04510 Mexico D.F., Mexico28 Facultad de Ciencias, Universidad de Chile, Casilla 653, Santiago, Chile29 Botany Division, D.S.I.R., Private Bag, Christchurch, New Zealand30 Laboratorio de Palinologia, National Universidad Mar del Plata, Departmento de Biologia,Funes 3250, 7600 Mar del Plata, Argentina31 Instituto de Geociencias, Fundacao Universidade do Brazilia, Campus Universitario, AsaNorte, 0910-900, DF Brazilia, Brazil32 Department of Geology, University of South Florida, Tampa, FL 33620, USA33 Seminar fur Geographie, Universitat zu Koln, Gronewaldstrasse 2, 50931 Koln, Germany

Received: 13 October 2008 – Accepted: 21 October 2008 – Published: 10 February 2009

Correspondence to: R. Marchant (rm524@york.ac.uk)

Published by Copernicus Publications on behalf of the European Geosciences Union.

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Abstract

The biomisation method is used to reconstruct Latin American vegetation at 6000±500and 18 000±1000 radiocarbon years before present (14C yr BP) from pollen data. Testsusing modern pollen data from 381 samples derived from 287 locations broadly repro-duce potential natural vegetation. The strong temperature gradient associated with5

the Andes is recorded by a transition from high altitude cool grass/shrubland and coolmixed forest to mid-altitude cool temperate rain forest, to tropical dry, seasonal andrain forest at low altitudes. Reconstructed biomes from a number of sites do not matchthe potential vegetation due to local factors such as human impact, methodologicalartefacts and mechanisms of pollen representivity of the parent vegetation.10

At 6000±500 14C yr BP 255 samples are analysed from 127 sites. Differences be-tween the modern and the 6000±500 14C yr BP reconstruction are comparatively small.Patterns of change relative to the modern reconstruction are mainly to biomes char-acteristic of drier climate in the north of the region with a slight more mesic shift inthe south. Cool temperate rain forest remains dominant in western South America. In15

northwestern South America a number of sites record transitions from tropical seasonalforest to tropical dry forest and tropical rain forest to tropical seasonal forest. Sites inCentral America also show a change in biome assignment to more mesic vegetation,indicative of greater plant available moisture, e.g. on the Yucatan peninsula sites recordwarm evergreen forest, replacing tropical dry forest and warm mixed forest presently20

recorded.At 18 000±1000 14C yr BP 61 samples from 34 sites record vegetation that reflects

a generally cool and dry environment. Cool grass/shrubland prevalent in south-east Brazil, Amazonian sites record tropical dry forest, warm temperate rain forestand tropical seasonal forest. Southernmost South America is dominated by cool25

grass/shrubland, a single site retains cool temperate rain forest indicating that forestwas present at some locations at the LGM. Some sites in Central Mexico and lowlandColombia remain unchanged in their biome assignments, although the affinities that

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these sites have to different biomes do change between 18 000±1000 14C yr BP andpresent. The “unresponsive” nature of these sites results from their location and theimpact of local edaphic influence.

1 Introduction

Biomisation is an objective method to reconstruct broad vegetation types based on the5

assignment of pollen taxa to one or more plant functional types (PFTs) (Prentice etal., 1996a). The method is based on the assumption that a pollen spectrum will havedifferent degrees of affinity to different biomes that can be quantified by a simple al-gorithm. Biome reconstructions from pollen data at 6000±500 14C yr BP and the lastglacial maximum (LGM) at 1000±18 000 14C yr BP have been produced for most re-10

gions of the world under the auspices of the BIOME 6000 project (Prentice et al., 1998,2000). The validity of the method in reconstructing biomes at different time intervalshas been demonstrated for Africa (Jolly et al., 1998a; Elenga et al., 2000), Australia(Pickett et al., 2004) Beringia (Bigelow et al., 2003; Edwards et al., 2000), China (Yu etal., 1998, 2001), Eastern North America (Williams et al., 2000), Eurasia (Tarasov et al.,15

1998a), Europe (Prentice et al., 1996a, b; Tarasov et al., 1998a, b; Elenga et al., 2000),Japan (Takahara et al., 2001) and Western North America (Thompson and Anderson,2000). Results from Latin America, presented here, represent the last geographicallylarge area to undergo this process. Within Latin America the biomisation method hasbeen previously applied to Colombian pollen data at a range of spatial and temporal20

scales; from the middle Holocene (Marchant et al., 2001a), the LGM (Marchant et al.,2002a), to investigate modern-pollen vegetation relationships (Marchant et al., 2001b),impact of human societies on vegetation (Marchant et al., 2004a) and as a basis forcomparisons with output from a vegetation model run under different climatic and envi-ronmental scenarios (Marchant et al., 2004b, 2006). In addition to these spatial investi-25

gations, the method has been applied down-core down to a 450 000 year pollen recordfrom the high plain of Bogota (Marchant et al., 2002b). As Colombia is biogeograph-

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ically complex, encompasses high altitude, temperate and tropical floras reflecting arange of environmental space including transitions from hyper-humid to semi-arid cli-mates, these analyses provided a suitable test-bed for the wider geographical focuspresented here.

In addition to reconstructing vegetation patterns, and investigating factors that can5

explain observed changes, data on past biomes contributes to testing of climate andvegetation models (Prentice et al., 1992; Haxeltine and Prentice, 1996; Peng et al.,1998; Marchant et al., 2006; Braconnot et al., 2007). Vegetation models can be usedto portray output from Global Circulation Models (GCMs) as maps of potential vegeta-tion (Claussen and Esh, 1994; Foley et al., 1996; Prentice et al., 1996b; Williams et10

al., 1998) that can be used in the development of models that couple biosphere, at-mosphere and oceanic components (Braconnot et al., 2007; Claussen, 1994; Harrisonet al., 2003; Texier et al., 1997) and testing of biogeochemical dynamics (Peng et al.,1998). There has been growing interest has focused how atmosphere-biosphere in-teractions have operated under the changing environmental conditions since the LGM,15

particularly in trying to understand the response of ecosystems to different types ofenvironmental forcing (Jolly and Haxeltine, 1997). Transformed pollen data can furtherbe used in conjunction with other data types, such as on lake status (Jolly et al., 1998b)and archaeological evidence (Piperno et al., 1990, 1991a, b), to better understand thecausal factors driving vegetation change over the recent geological past.20

1.1 Latin American region

Latin America comprises the area from 35◦ N to 65◦ S, and from 35◦ W to 120◦ W ex-tending from Mexico to islands off southernmost South America from eastern Brazil tothe Galapagos Islands. Latin America is characterised by strong environmental gradi-ents associated with 100◦ of latitude, approximately 7000 m of altitude and the transition25

from oceanic- to continentally-dominated climate systems (Fig. 1). Physiographically,Latin America is characterised by stable cratons associated with the interior and areasof active mountain building, particularly associated with the Andes. This environmental

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variability is reflected by an incredibly diverse biogeography, ranging from the highlydiverse rain forest of the Choco Pacific (Colombia) to the cold deserts of the high An-des, from the hot semi-desert areas of Mexico to the cold moorlands of Tierre delFuego (Fig. 2). Descending an altitudinal gradient there is a transition from paramo(cool grass/shrubland) to high Andean forests (cool mixed and cool temperate rain5

forests) and lower Andean forest (warm evergreen forest) (Fig. 3). Complicating thispotential vegetation distribution is the factor of human impact with the majority of thevegetation in Latin America being impacted on by the vegetation (Ellis and Ramankutty,2008). The timing of early human settlement in Latin America is a contentious subject,although it seems from the early Holocene there was considerable cultural diversity10

and adaptation to a series of different environments (Gnecco, 1999). Human-inducedimpact has had a direct influence on vegetation composition and distribution throughland-use practices and the introduction of alien taxa and cultivars to the Latin Americanflora. For example, in excess of 100 plants were under cultivation prior to the Europeanconquests in the 15th century (Piperno et al., 2000).15

1.1.1 Latin America climate

Cerverny (1998), Eidt (1968) and Metcalfe et al. (2000) have reviewed Latin Ameri-can climate. Given the broad geographical scope, Latin America is characterised bya variety of climates that relates to its global position, shape of the landmass, locationand height of the Andes, offshore currents, general hemisphere air flow and proxim-20

ity of large water bodies (Fig. 1). Four dominant circulation regimes influence LatinAmerica: the Inter-Tropical Convergence Zone (ITCZ), the prevailing westerlies, thesemi-permanent high pressure cells located over the South Pacific and South AtlanticOceans and the trade winds. Perhaps most dominant is the annual oscillation of themeteorological equator (ITCZ), this migrating some 10–15◦ latitude about the equator25

(Fig. 1). The ITCZ reaches its northernmost location in June, this bringing high rainfallfor northern South America and the Caribbean, with January and February recordingthe dry season (Cerveny, 1998). However, due to the influence of the westerlies from

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the Pacific, and the sharply rising topography of the Andes, the ITCZ has a sinusoidalprofile over northwestern South America (Fig. 1). In southern South America the pre-vailing westerlies south of 40◦ S are particularly important in controlling the moistureregime. The topographic barrier of the Andes contributes to the creation of two largesemi-present anticyclones, one over the South Pacific and one over the South Atlantic,5

the southeast trade winds associated with this latter system brings abundant moistureto the Amazon Basin (Cerveny, 1998). Due to the large size of South America, and thehighland ranges that fringe much of the continent there is often a rapid transition fromrelatively moist coastal areas to a dry interior reflecting the transition from oceanic- tocontinental-dominated climate systems. For example, due to the proximal location of10

the Pacific-based moisture source and steeply rising ground, precipitation is highest(>15 000 mm yr−1) in the Choco Pacific region. Exceptions to this scenario are areaslocated between the anticyclones, e.g. the Peruvian coast, where relatively arid condi-tions prevail.

One of the main environmental gradients in Latin America is associated with the15

Andes. The Andes are characterised by a diurnal climate (Kuhry, 1988); at a given lo-cation differences in monthly temperature are small (<3◦C) although daily fluctuationsmay be large (20◦C), especially during the dry seasons. Climatic changes with altitudecan be summarised as a lapse rate (Barry and Chorley, 1990). Applying a lapse rate of6.6◦C 1000 m−1 (Van der Hammen and Gonzalez, 1965; Wille et al., 2001), this altitudi-20

nal rise equates to a temperature change of more than 30◦C. Also associated with theAndes are steep gradients of moisture availability. Rainfall is high on the eastern slopesof the Andes; the concave nature acting as a receptacle for moisture transferred by thesoutheast trade winds from the Atlantic Ocean, in part receiving moisture generated bythe Amazonian forest (Fjeldsa, 1993). Low rainfall is recorded within rain shadow ar-25

eas, such as on the lower slopes of the Magdalena Valley and the inter-Andean plains(Kuhry, 1988). These climate gradients result in rapid transitions from mesic to xericvegetation types, e.g., cool high-altitude grasslands change to “temperate” forests atmid-altitudes and diverse tropical rain forests within a few kilometres (Fig. 3). In south-

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ern part of Latin America rainfall is largely controlled by the persistence and strengthof the westerly winds (Gilli et al., 2005).

In recent years there has been increased interest in large-scale temperature-drivensurface pressure oscillations in the Pacific Ocean termed the Southern Oscillation, andits assimilated oceanic aspects, El Nino and its antithesis La Nina (Cerveny, 1998).5

The climate of the tropical Pacific basin, extending from the western Americas acrossto Australia, New Zealand, and northeast Asia, oscillates at irregular time intervals (3to 7 years) between an El Nino phase, with warm tropical waters upwelling off Pacificcoastal South America, and a La Nina phase, with cold tropical waters dominating.ENSO events are the largest coupled ocean-atmosphere phenomena resulting in cli-10

matic variability on inter-annual time scales (Godınez-Domınguez et al., 2000). Asclimates, particularly rainfall patterns, are driven by temperature differences betweenland and ocean, the influence of changing oceanic sea surface temperatures (SST) oncoastal South American environment can be dominant, and have a strong influenceelsewhere (Marchant and Hoohiemstra, 2004). El Nino events primarily result in in-15

creased precipitation along the Pacific coastal regions, decreased precipitation withinlowland tropical moist forests of Central America (Cerveny, 1998) with increased pre-cipitation in northern Central America (Metcalfe et al., 2000).

1.1.2 Latin America vegetation

For the purpose of this investigation the potential vegetation composition and distribu-20

tion Latin America is classified at a coarse resolution with twelve biomes being iden-tified (Fig. 2) that summarise the 57 categories mapped by Huck (1960) and 45 bySchmithusen (1976). The vegetation composition and distribution generally reflectsthe main climatic and topographic gradients described above. However, a series ofcaveats to this must be stressed. Firstly, the actual and potential vegetation can be25

quite different, the former reflecting a long history of human interaction that has beenparticularly pronounced since the colonial period but has been influencing the vege-tation for at least the last 5000 years (Marchant et al., 2004). In numerous areas this

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interaction has completely transformed the potential vegetation to an agricultural land-scape. Another factor complicating the relationship between climate and vegetation isthe locally strong edaphic influence by substrate, topography or geographic character(Fig. 3). The strength of this influence is characterised by areas of tropical dry forestthat forms on free-draining sandstones, e.g. the Llanos Orientales (Colombia); these5

are located in areas where the climate regime would support tropical seasonal forest,or even tropical rain forest. The vegetation at such locations is relatively insensitive toclimate changes as these must be of a greater magnitude than the influence impartedby the edaphic factor.

Broad types of vegetation with similar composition and distribution (biomes) result10

from a combination of plant functional types (PFTs). PFTs and biomes, which lie atthe heart of the biomisation technique, allow the high floristic diversity of the LatinAmerica pollen flora to link with the relatively coarse vegetation classification (Fig. 4).PFTs group together species that have common character (Prentice et al., 1992). Thisgrouping is based on common life form and phenology, combined with the geographic15

distribution that is in part determined by climate (Woodward, 1987). An indication ofthe bioclimatic range of each PFT and plant physiological adaptation, to the given en-vironmental condition, is presented in Table 2. The range of biomes identified withinthe Latin America, floristic description, main location and equivalent floristic units isportrayed in Table 3. The cool grass/shrubland biome incorporates a relatively wide20

range of vegetation dominated by grasses, heath, cool temperate sclerophyll shrubsand cushion plants (Fig. 3). This biome is present in southern South America and athigh altitudes along the Andes. In addition to the cool grassland, a warm grassland(steppe) is identified. Steppe is found predominately under the warm, dry climates ofsoutheast and northeast Brazil, northwestern Argentina and coastal northern South25

America. Warm temperate rain forest represents a mix of warm conifers such as Arau-caria, Andean and Atlantic rain forest elements, whereas cool temperate rain forestcontains cool conifers, such as Fitzroya, Andean and Valdivian rain forest elements.Dry forests are extensive in Latin America, specifically associated with areas located

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between the two semi-permanent anticyclones and influenced by the high seasonal-ity of rainfall imposed by the annual migration of the ITCZ. For our classification wecharacterise the diverse dry vegetation formations (Fig. 4) as the tropical dry forestand xerophytic trees and shrub biomes. Xerophytic trees and shrubs is widespreadin the interior of South America, along the southwestern Pacific coast and northeast5

Brazil where it grades into steppe, additionally, there are patches in Colombia, on theYucatan peninsula and in Mexico (Fig. 2). Tropical dry forest is predominantly recordedin two main swaths either side of the Amazon basin, with an extension through CentralAmerica. The tropical seasonal forest biome is predominantly recorded to the north ofAmazonia where it is interspersed with patches of dry forest; this reflecting a strong10

edaphic influence. A large area of tropical seasonal forest is recorded away from thehyper-humid area of Brazil along the Atlantic coast. The tropical rain forest biomeis present in three main areas: Amazonia, linear strips along the Atlantic coast andnortheast South America extending into Central America. Forest associated with high-land areas is divided into three biomes: warm evergreen forest, cool temperate rain15

forest and cool mixed forest (Fig. 4). Warm evergreen forest is most extensive alongthe lowland Andes, adjacent to the tropical rain forest. Cool mixed forest has a morerestricted distribution, occupying a highland position until temperature becomes limit-ing for a number of taxa. Warm mixed forest is characterised by a mix of Pinus andQuercus species and is mainly restricted to Central America. The desert biome is re-20

stricted to coastal Peru, due to the Pacific Ocean anticyclone, this area receives verylittle moisture, except when the area is subjected to El Nino events.

2 Methods

2.1 Data sources

Over the past five decades palynologists have collected numerous pollen-based25

records from lakes and bogs (Table 1) that have been used to unravel past vegeta-

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tion changes in Latin America with ecosystem reconstructions now existing from allmajor vegetation types over the Late Quaternary period. The Latin American PollenDatabase (LAPD) (www.ncdc.noaa.gov/paleo/lapd.html) is an online resource used tocollate these data and facilitated the systematic interoperation presented here; indeedthe majority of the pollen data used here are available through the LAPD. Additional5

data were obtained from palynologists working in Latin America; all active palynolgistsbeing given the opportunity to contribute data not currently lodged in the LAPD. Indeed,data from a number of sites in Argentina, Brazil, Costa Rica, Mexico and Panama weremade available specifically for this work. The majority of data from Colombia were pre-pared for this analysis directly from the original count sheets and are in preparation for10

uploading to the LAPD.The majority of the data used in our analysis are complete raw pollen counts, this

permitted all pollen taxa recorded by the original analyst to be allocated to PFTs andallowed the integrity of the data to be maintained throughout the analysis. Applicationof raw pollen data in other regions has been shown to help in differentiating between15

biomes (Tarasov et al., 1998b). However, numerous pollen records are either not sub-mitted to the LAPD, or, were not made available for this analysis. Rather than omittingthese data, the pollen counts were digitised from published pollen diagrams (Table 1):digitising such data provides a spatially more complete reconstruction than availablefrom presently archived data. The process of digitisation involved either back calcula-20

tion of the pollen counts if information on the pollen sum was present. If the pollen sumwas not available the pollen percentage diagram was used as a count of 100 and val-ues for the pollen taxa were abstracted at the time intervals used for our analysis. Thisscenario of combining data from different formats comes with a number of caveats thatcan have bearing on the results, and their interpretation (Marchant and Hooghiemstra,25

2001). Firstly, the sub-set of pollen taxa in a count used to construct published pollendiagrams, and pollen sums that comprise it, often result from the bias of individual re-searchers’, particularly on what are the reliable indicator taxa for a particular area andrange of different vegetation types under investigation. This issue is particularly crucial

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in Latin America where the large numbers of pollen taxa encountered in the originalcounting are rarely depicted on published pollen diagrams. Furthermore, the level ofidentification achieved within pollen analysis, to a generic or family level, commonlycomprises species that can be found in a range of different vegetation types, ecolo-gies and growth forms (Marchant et al., 2002c). The majority of the samples for the5

biomisation presented here are derived from sites close to the Andean spine. Primarily,this concentration reflects the sensitive response of the vegetation to climate changeon the steep altitudinal gradients (Marchant et al., 2001b); the area forming an ideallocation for palaeoecological research. Additionally, the comparative lack of data fromthe lowlands is fuelled by problems of access, suitable sites and strong river dynamics10

that commonly result in sedimentary hiatuses (Ledru, 1998). This spatial bias of thelocation did not reduce the number of biomes we were able to reconstruct, because ofthe steep environmental gradients associated with 7000 m of altitudinal change foundalong the Andes (Fig. 4).

Uncalibrated radiocarbon dates available from the original stratigraphic analysis were15

used to select samples representing the time period used here; these were. On asite-by-site basis, a linear age-depth model was applied to the pollen data. The va-lidity of this model was assessed at each site taking into account sedimentary hia-tuses and dating problems such as age reversals and dates with large standard er-rors; a summary of this dating control is provided in Table 6 following the COHMAP20

scheme (Webb, 1995; Yu and Harrison, 1999). Multiple samples (≤3) were selectedwhen more than one sample fell within the age range allowed for each time period.These data were compiled, to produce a site vs. taxa matrix that was then checkedto standardise nomenclature, e.g., the combined file contained many synonyms suchas Gramineae and Poaceae, and Mysine and Rapanea. Synonymous taxa were com-25

bined using the nomenclature of Kewensis (1997) and the International Plant NamesIndex (IPNI) (1999). Aquatic and non-arboresent fern taxa were removed from thematrix as they commonly reflect local hydrological conditions rather than local climateenvelope. Marker additions and exotic spikes such as Lycopodium were also removed.

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A total of 381 samples from 287 locations derived from core tops (<500 14C yr BP),surface samples, pollen traps and moss polsters comprise the modern data set (Ta-ble 1). For the time period 6000±500 14C yr BP, 255 samples derived from 127 pollenrecords comprise the data set (Table 1). For the time period 18 000±1000 14C yr BP,61 samples derived from 34 pollen records comprise the data set (Table 1). The data5

sets to undergo analysis comprised 515 pollen taxa for the modern calibration, 493 forthe 6000 14C yr BP reconstruction and 232 for the 18 000 14C yr BP reconstruction. Thetaxonomic diversity of the Neotropical phytogeographical realm can be demonstratedby taking the modern biomisation as an example: the number of pollen taxa for theproduction of our biomes is greater than Africa (364) (Jolly et al., 1998a), Europe (41)10

(Prentice et al., 1996b), Russia and Mongolia (98) (Tarasov et al., 1998a) and China(68) (Yu et al., 1998).

2.2 Biomisation

Prentice et al. (1996a) and Prentice and Webb (1998) have documented the stepsinvolved in the biomisation technique. First, a conceptual framework for biomes and15

PFTs in Latin American vegetation was developed by investigating the relationship be-tween potential biomes and three environmental gradients. The environmental gra-dients considered were moisture availability (α: Priestley-Taylor coefficient of plantavailable moisture), temperature (MTCO: mean temperature of the coldest month) andseasonal warmth (GDD: growing degree-days). To enable a definition of the biomes20

to be based on bioclimatic data, rather than qualitative assessment, the climate spaceencompassed by Latin America was plotted against climate data set of Leemans andCramer (1991) with relationship between biomes at individual site locations and macro-scale climate changes (α and MTCO) investigated in two-dimensional space (Fig. 5).The twelve biomes identified within Latin America (Table 2) are designed to incorpo-25

rate the range of major vegetation types and ensure consistency with previous areasto undergo the process within the BIOME 6000 community.

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Similarly to the biomes, but on a finer ecological resolution, the spatial distributionof PFTs is determined by environmental controls on plant growth form and ecologicaltolerance (Woodward, 1987). In Latin America the dominant environmental gradientsare temperature, primarily associated with altitude, moisture availability and season-ality. PFT definitions were modified from the classification originally developed for the5

BIOME 1 model (Prentice et al., 1992, 1996a, b) taking into account schemes devel-oped for other regions, particularly those that abut the Latin American region or containsimilar floristic elements (Jolly et al., 1998a; Pickett, et al., 2004; Takahara et al., 2001Elenga et al., 2000; Thompson and Anderson, 2000; Yu et al., 2000). Five main groupsof PFT were distinguished: these containing tropical (non-frost tolerant), coniferous10

(needle-leaved), temperate (frost tolerant), xerophytic (drought tolerant), and frost anddrought tolerant taxa (Fig. 6). This latter group is present in cold dry conditions ofsouthern South America and the high Andes. A sixth “miscellaneous” group repre-sents various life forms with restricted diagnostic value. The Latin American flora wasdivided into 25 PFTs (Table 3). The PFTs, although being ecological distinct, can be15

multiply assigned to the biomes (Table 4). The classification is based on the originalscheme devised for the Biome 3 vegetation model (Prentice et al., 1992) and modifica-tion through regional applications to pollen data. Where possible the scheme devisedfor Latin America conforms to existing classification and definitions. However, someof the specific vegetation types in Latin America were not adequately covered by the20

existing range so two new PFTs (heath and cushion plants) were added. To aid in theseparation of the African forest/savanna boundary, Jolly et al. (1998a) subdivided thetropical raingreen trees PFT (Tr) into three groups. In the case of Latin America, it wasdecided that the overlap (taxa being multiply assigned to the PFTs) between the PFTswould be too great, and the distinction somewhat minimal. Furthermore, the tropical25

xerophytic trees and shrubs PFT encompass many taxa that would be assigned to thedriest tropical raingreen category. Therefore, the Tr PFT was subdivided into “wet” (Tr1)and “dry” (Tr2) tropical raingreen trees.

The cornerstone of research concerned with the composition and distribution of Latin

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American vegetation, be it in a contemporary time frame, or one that aims to work inthe past, is a good understanding of the ecology and distribution of the taxa concerned.The Latin American pollen taxa were assigned to one or more PFTs depending on themodern ecological range of the most important (i.e. most abundant) taxa responsiblefor producing the pollen identifiable within the modern data set. These assignments5

were made following reference to the known biology of plants from several floras (Rze-dowski, 1983; Schofield, 1984; Wingenroth and Suarez, 1984; Kahn and de Granville,1992; Gentry, 1993; Maberly, 1993; Seibert, 1996), botanical and palynological stud-ies (Beard, 1955; van der Hammen, 1963, 1972; Wijmstra and van der Hammen,1966; Eiten, 1972; Cleef and Hooghiemstra, 1984; Hooghiemstra and Cleef, 1984;10

Pires and Prance, 1985; Prance, 1985; Cuatrecasas and Barreto, 1988; Brown andLugo, 1990; Bush, 1991; Dov Par, 1992; Kappelle, 1993; Duivenvoorden and Cleef,1994; Witte, 1994; Armesto et al., 1995; Harley, 1995; Kappelle, 1995; Kershaw andMcGlone, 1995; Veblen, et al., 1995; Colinvaux, 1996; Grabherr, 1997; Hooghiem-stra and van der Hammen, 1998) and personal communication with modern ecologists15

and palaeoecologists. Much of this information has been collated into a dictionary onthe distribution and ecology of parent taxa of pollen lodged within the Latin AmericanPollen Database (Marchant et al., 2002c). The resultant taxon vs. PFT assignmentsare presented in Table 5. Due to the high intra-generic diversity, and also the widerange of ecology’s exhibited by the parent taxa present within some genera, a number20

of taxa were multiply assigned to a number of PFTs; where possible, pollen taxa wereassigned to the PFTs within which the parent taxa are most common.

Thus, the identified PFTs from Latin America are described by the suite of pollentaxa assigned to them, in turn the biomes are distinguished by the suite of constituentPFTs. A number of pollen taxa belong to more than one PFT, and, as is the case25

with the potential vegetation, most PFTs contribute to more than one biome. Twoproblems can arise here for our analysis that can be circumvented by manipulation ofthe input matrices and output biome affinity scores. First, pollen samples can haveequal maximum affinity with more than one biome; this commonly occurs when the

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PFTs characteristic of one biome are a subset of another biome. Assigning the biomesso that subsets always come first in the analysis solves the problem. A second problemarises where multiple samples encompass the age boundaries. Multiple samples froma single site may have maximum affinity to a number of different biomes; the chanceof this is high when the score of the “best” biome is close to that of the next “best”.5

In such cases, the “majority” biome is mapped. For example, site A contains eightsamples within the time frame of 6000±500 14C yr BP, five samples have the greatestaffinity to biome 1, two samples to biome 2 and one sample to biome 3. The result isthat biome 1 is mapped for site A at 6000±500 14C yr BP.

Biomes were reconstructed from pollen data at sites with surface sample, trap and10

radiocarbon-dated core-top data. The results were used to produce a modern pollen-derived biome dot map (Fig. 7); for each site a colour dot records the reconstructedbiome with the highest affinity score. These were compared site by site, with the po-tential modern vegetation distribution (Fig. 2). The biomisation procedure was appliedto the fossil datasets without modification. Results for all sites and periods are pro-15

vided in Table 6 which allows a site-by-site comparison through time and a comparisonbetween the modern reconstruction and potential vegetation.

3 Results

3.1 Modern pollen vs. potential biome reconstruction

Visual comparison shows that the biomes reconstructed from modern pollen data20

(Fig. 7) accurately reflect the broad features in the potential vegetation map (Fig. 2). Inparticular the modern reconstruction correctly reproduces the transition from relativelymesic vegetation types, around the coastal areas of South America, to the more xericbiomes towards the interior. For example, in eastern Argentina there is a transitionfrom steppe to xerophytic woods and scrub. Warm temperate rain forest is an impor-25

tant biome in the southern and southeastern Brazilian highlands, with tropical dry forest

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being reconstructed towards the interior. Notable from this region is the large numberof different biomes being reconstructed in a relatively small area. In part this reflectsthe variability of potential vegetation, not portrayed in our relatively coarse resolutionvegetation map (Fig. 2). For example, a site recording tropical rain forest reflects thesites’ lowland position where it is characterised by moist gallery forest with a number of5

typical rain forest taxa present. Steppe is correctly reconstructed from the grasslandsof south-eastern Argentina and dry forest in central Argentina, mirroring the transitionto “drought-deciduous thorn forests” of central Argentina (Schmithusen, 1976). Steppeis assigned farther west at approximately 1000 m in the Andes, southernmost SouthAmerica and northeast Brazil. The vegetation of southern South America is dominated10

by cool temperate rain forest. The failure of the analysis to pick up the transition fromcool temperate rain forest to cool grass/shrubland as one progresses east along Tierredel Fuego stems from the pollen spectra having a relatively large amount of Nothofa-gus pollen. Moving northwards from southern South America there is a transition tocool mixed forest, cool grass/shrubland and steppe; these latter assignments are par-15

ticularly associated with eastern flanks of the Cordillera de los Andes.The concentration of sites along the Andes results in a wide range of reconstructed

biomes being geographically adjacent to each other when mapped in two-dimensionalspace (Fig. 7). This phenomenon is most apparent in the northern Andes where the al-titudinal, and therefore climatic gradients are at their steepest. Despite these rapid en-20

vironmental changes, the biome assignments reflect the changing vegetation patterns.There is a clear altitudinal transition: low altitudes (<300 m) being mainly assigned tothe tropical rain forest, tropical dry forest, tropical seasonal forest and steppe. Sites lo-cated at mid altitudes are described by a number of different biomes including tropicalseasonal forest, warm mixed forest, cool mixed forest and cool temperate rain forest.25

Within this wide range of warm temperate rain forest and tropical seasonal forest arecommonly assigned at lower elevations (Fig. 7, Table 6). Many of the sites at high alti-tude have a high affinity to the cool grass/shrubland biome. The line of the Andes canbe easily seen by the cool grass/shrubland biome assignments, these being commonly

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recorded at sites above 3800 m. The warm temperate rain forest is assigned at lowerelevations and is analogous to Andean forest, being dominated by Podocarpus, Quer-cus and Weinmannia and comprises a different forest type to that assigned in southernand southeast Brazil or southern South America. A mixture of biomes presently char-acterises the Amazon Basin with only four sites recording the tropical rain forest biome;5

two of these are in coastal locations. Tropical seasonal forest is recorded in four loca-tions; this representing a slightly drier type of forest than tropical rain forest, containingsome deciduous taxa. A number of “Amazonian” sites record warm temperate rain for-est, these assignments responding to the presence of Andean floristic elements withinlowland vegetation. There are a number of sites that record tropical dry forest, this10

being relatively widespread, e.g. on Easter Island, lowland Colombia and the Brazilianinterior. Warm temperate rain forest describes the majority of the sites in the Panama-nian and southern Costa Rican isthmus with warm mixed forest, being commonly athigher altitudes. This is an area where the comparison between the observed and pre-dicted biomes shows a discrepancy; the possible reasons behind this will be discussed15

fully. Warm mixed forest is correctly assigned to the highlands of central and southernMexico as is tropical dry forest on the Yucatan peninsula of southern Mexico.

Investigating the correspondence between the pollen-based reconstruction and thepotential vegetation for individual biomes provides a check on the methodology, partic-ularly the construction of the matrices. The cool grass/shrubland biome is accurately20

reconstructed at the majority of sites. The sites that do not match the potential vege-tation commonly result from the inclusion of high altitude arboreal pollen, this resultingin assignments of cool mixed forest and cool temperate rain forest. The other com-mon assignment is towards steppe; the dominance of the pollen spectra by Poaceae,and lack of shrubby taxa, result in the assignment to the steppe biome. Indeed, the25

affinity scores to the cool grass/shrubland and steppe biome at most sites, where oneof these biomes is dominant, is normally quite close. For the cool mixed forest biome66% of sites accurately reconstruct the potential vegetation. The 34% of “wrong” as-signments mainly result in either a reconstruction of cool grass/shrubland, thought to

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represent possible forest clearance, and the dominance of the vegetation by grass-land, or cool temperate rain forest biome due to the numerous shared taxa betweenthese two biomes. 75% of cool temperate rain forest biome reconstructions match thepotential vegetation at the site. The remaining 25% of the sites mainly show eitherwarm mixed forest or warm temperate rain forest assignments. For the tropical dry5

forest biome some 90% of the sites accurately reflect the potential vegetation at thesite. For the remaining 10% of “wrong” assignments the common result is towards acool temperate rain forest or steppe. For the tropical rain forest biome 85% of the sitesaccurately reflect the potential vegetation. For the sites that do not match, a commonreconstruction is warm temperate rain forest. This can be explained by the number of10

Andean elements being present within lowland tropical forests with a couple of sitesreconstructing the closely related biome of tropical seasonal forest. This facet of thepollen data is also exemplified by a number (35%) of the tropical seasonal forest sitesrecording the warm temperate rain forest biome. Warm evergreen forest is correctlyassigned at 80% of the sites. Warm temperate rain forest is assigned correctly at 78%15

of sites. 75% of the sites that do not reconstruct this biome “correctly” lead to assign-ments of tropical rainforest and tropical seasonal forest at low altitudes (<500 m) andcool mixed forest at high altitudes (>2000 m) .

The generally correct biome assignments, in relation to a map of potential vegetationconfirm the robustness of our application of the biomisation method to Latin America.20

Where the match between pollen and potential vegetation reconstructions is relativelylow (tropical seasonal forest and warm temperate rain forest), then a common forcingfactor, that of “high altitude” plants presently growing at low altitudes appears important.For other sites where the reconstructed biome does not match the potential vegetationmap a series of different explanations, particularly local site-specific factors such as25

human impact, can be invoked, these will be discussed fully. Taking our modern pollento potential vegetation calibration, and the design of the matrices that drive it, we re-construct vegetation at past time intervals with “cautious confidence”.

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3.2 6000 14C yr BP biome reconstruction

The biomes reconstructed at 6000±500 14C yr BP (Fig. 8) show relatively small patternsof change compared to the present. 85% of the sites retain the same biome assign-ment as present (Table 6). In southeastern Brazil, the majority of the sites that werepreviously assigned to the warm temperate forest biome remain unchanged. A number5

of sites (e.g. Serra Campos Gerais, Rio Sao Francisco, Aguads Emendadas) recordtropical dry forest at 6000±500 14C yr BP replacing tropical seasonal forest (LagunaAngel, Laguna Chaplin) record at the present. Sites assigned to tropical rain forestand tropical seasonal forest today mainly remain unchanged at 6000±500 14C yr BP.Steppe continues to be reconstructed in southeastern Argentina, as today. However,10

a number of sites had substantially more arboreal components at 6000±500 14C yr BPthan today, for example Empalme Querandıes and Lake Valencia show a transitionfrom steppe to tropical dry forest. An expansion of steppe is recorded at sites pre-viously assigned to cold mixed forest on the Cordillera de los Andes. Unlike sites insouthern-most South America that similarly record steppe, these sites also contain15

significant amounts of Alnus and Podocarpus indicative of “parkland” at this time. Oncloser inspection of the affinity scores, sites record an increased affinity to cool tem-perate rain forest, primarily due to increased amounts of Nothofagus pollen, althoughthis was not sufficiently numerous to produce a cool temperate rain forest assignment.Southernmost South America continues to have a mixture of cool mixed forest, cool20

grass/shrubland, steppe and cool temperate rain forest biomes, the latter being domi-nant. Along the southern Andean spine, the assignments do not differ greatly from themodern assignment. There are broadly similar assignments to the present at Colom-bian sites although there is a slight increase in the number of cool mixed forest andcool temperate rain forest biome assignments relative to cool grass/shrubland of the25

present day. The sites where this occurs (e.g. La Primevera, and Paramo de PenaNegra) are located at high altitude sites and may reflect either a lowering of the forestline or increased distribution of Andean forest that predates early human impact. Sites

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in coastal northern South America show a transition to tropical dry forest and tropicalseasonal forest from steppe and tropical dry forest respectively, both indicative of arelatively mesic environment. A number of sites on the Yucatan peninsula show a cleardistribution of warm evergreen forest at 6000±500 14C yr BP changing from the warmmixed forest and tropical dry forest reconstruction for the present day. These transi-5

tions are not recorded everywhere, for example sites located in the Mexican highlandsretain the same biome assignment at the present – warm mixed forest.

3.3 18 000 14C yr BP biome reconstruction

Vegetation at 18 000±1000 14C yr BP was substantially different from the present-day,or that reconstructed at 6000±500 14C yr BP (Fig. 9). The intensity of this vegetation10

transformation is demonstrated by 82% of the sites change the biome assignment rel-ative to the two previous periods. In Amazonia, tropical seasonal forest and tropicaldry forest is recorded instead of tropical rain forest or tropical seasonal forest recon-structed for the present. A site on the present southern Amazonian boundary (LagunaChaplin) records tropical seasonal forest. Sites in southern South America nearly all15

sites show a transition from cool mixed forest biomes and cool mixed forest to coolgrass/shrubland and cool grassland. However, within this homogenous reconstructiona number of sites have a relatively high affinity to the cool temperate rain forest biome,due to a mix of Donartia and Nothofagus pollen: this explains why the northernmost sitein this cluster records cool temperate rain forest. Sites in southeastern Brazil record a20

transition from tropical dry forest to tropical seasonal forest and cool grass/shrubland.Sites in Amazonia record mainly tropical seasonal forest, warm temperate rain forest orsteppe; this combination indicating relatively mesic forest. In the Colombian lowlands,tropical dry forest continues to be assigned whereas Colombian highland locations re-flect a marked change from cool temperate rain forest and cool mixed forest to the25

cool grass/shrubland biome. Sites in Central America show a change from tropicalseasonal forest to tropical dry forest, e.g. El Valle. The Mexican highland sites remainunchanged, continuing to support warm mixed forest with the pollen records being

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dominated by Pinus and Quercus.

4 Discussion and conclusions

Previous applications of the biomisation method in Africa (Jolly et al., 1998a; Elenga etal., 2000), China (Yu et al., 1998, 2001), Australia (Pickett et al., 2004), Eastern NorthAmerica (Williams et al., 2000), Eurasia (Tarasov et al., 1998a), Europe (Prentice et5

al., 1996a, b; Tarasov et al., 1998a, b; Elenga et al., 2000), Japan (Takahara et al.,2001) and Western North America (Thompson and Anderson, 2000) demonstrate thattechnique is able to translate multi-site pollen data to coarse resolution vegetation re-constructions that works well over a range of vegetation types. The Latin Americanresults presented here provide a further test of this ability. The ability of the biomisa-10

tion method to reconstruct biomes derives in part from the relatively coarse vegetationclassification (Fig. 2); which conceals significant intra-biome variation; for example, wedo not distinguish subtypes of the warm evergreen forest biome which contains Arau-caria in southern and southeastern Brazil and Podocarpus in the northern Andes. Thesuccess of the biomisation technique is in part be due to reconstructions being carried15

out at a regional scale, allowing the methodology to be adapted to the local flora, bio-climatic gradients and pollen spectra. For example, the treatment of Quercus pollenin Latin America is quite different from that in a European context. Similarly, in AfricaPodocarpus is assigned to the warm temperate broad-leaved evergreen PFT (Jolly etal., 2001), although this taxon is a coniferous needle-leafed tree, in Latin America it is20

assigned to cool and intermediate temperate conifers. This regional focus also allowsthe pollen to plant functional type allocations to be based on good ecological informa-tion concerned with environmental tolerances to growth limits and an understandingof how representative the pollen is of the surrounding vegetation. This is particularlyimportant as the pollen taxa identified to the generic level (the taxonomic level usu-25

ally identified to) exhibit considerable plasticity in their growth form and environmentaltolerance. For example, within the genus Cordia, commonly a woody shrub of open

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thorn woodland, of the northern Andes (Cleef and Hooghiemstra, 1984) two species ofCordia are herbs in cerrado (Pereira et al., 1990; Sarmiento, 1975), the genus is alsopresent (C. lomatoloba and C. sagotii) in Amazonian terra firme forest and Guyaneselowland rain forest (Steege, 1998). Furthermore the specific nature of pollen produc-tion, dispersal and incorporation into a sedimentary environment exhibits considerable5

variability that is part dependent on site characteristic. All these factors have a bearingon the results and need to be considered in the designing of the input matrices into thebiomisation process and interpretation of results.

Biomes are mainly accurately reconstructed for the present-day even though largeareas of Latin America are covered with vegetation that has been altered by a long10

history of human land use (Behling, 1996; Binford et al., 1987; Fjeldsa, 1992; Gneccoand Mohammed, 1994; Gnecco and Mora, 1997; Marchant et al., 2004; Northrop andHorn, 1996). One possible reason for this may relate to the nature of the “modern”samples. Within our analysis the modern samples are largely derived from sedimen-tary columns rather than surface trap pollen data, and hence they may stem from the15

last 500 years and be reflective of a period prior to intensive human-induced change.However, the signal of vegetation clearance does impact on the modern reconstructionas shown by the large number of sites recording cool grass/shrubland, particularly atlower and mid-altitudes that should support cool mixed forest or cool temperate rainforest. These assignments are thought to result from human impact with the pollen20

spectra being dominated by Poaceae and hence recording more open vegetation. Toquantify the nature of this impact, it is possible to tailor the biomisation methodology toinclude elements of the pollen spectra, such as agricultural and ruderal taxa, that mayindicate human impact (Marchant et al., 2002). The ability to reconstruct potential,rather than actual vegetation, may also relate to the type of impact; although spatially25

relatively widespread, forest clearance is often only partial with many localised patchesof forest and secondary vegetation remaining. This results in the floristic compositionof the remaining vegetation, in palynological terms at least, closely reflecting the origi-nal vegetation composition. For example, the forest surrounding the Fuquene-II site is

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a successional type of forest whereas the natural vegetation would be a Andean foresttype dominated by Quercus and Weimannia mixed with Croton, Oreopanax and Phyl-lanthus (Van Geel and Van der Hammen, 1973). In addition to the relatively coarsepotential vegetation and biome classification, mapping the highest biome affinity scoreto each site as a single dot also allows the method to be relativity robust. Although this5

is suitable for the relatively coarse reconstructions necessitated by the continental/sub-continental scale, when investigating a small area, more information can be preservedfrom the analysis. Indeed at a regional scale information on sub-dominate biomescan be kept (Marchant et al., 2001a), new more defined biomes (Bigelow et al., 2003)or at a site-specific scale where the affinity scores in all the biomes can be retained10

(Marchant et al., 2001b, 2002b).Despite the overall agreement between potential and reconstructed biomes a num-

ber of locations show anomalies. Due to the floristic and structural similarities betweenwarm and cool grasslands (Tarasov et al., 1998a), grass-dominated biomes can beparticularly difficult to distinguish from one another. Differentiation is possible by the15

other plants within steppe and cool grass/shrubland, although there remains a highaffinity score to the cool grass/shrubland at low altitudes with the reverse for the steppebiome at high altitudes. Another facet is that some lowland sites show reconstructionsof highland biomes, e.g. sites in central Panama and Amazonia recording warm tem-perate rain forest. This result is driven by the presence of genera that are typical of20

montane vegetation, e.g. Hedyosmum, Podocarpus and Quercus. A possible explana-tion for the presence of these highland elements is that they are relictual; relatively iso-lated today they were previously much more widespread under the glacial climate normof the Quaternary. This suggestion is supported by the similarity, at a generic level, ofthe flora in highland Brazil and the northeastern Andes, and the isolated patches of25

savanna within Amazonian forest and the Brazilian cerrado. Furthermore, it is interest-ing that the presence of highland elements appears to be greater when the moisturelevels are high. For example, within the Choco Pacific region, where rainfall exceeds15 000 mm yr−1, montane elements appear more common than within Amazonia (Gen-

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try, 1986).Notwithstanding some of the anomalies mentioned, the biomisation method applied

to Latin American pollen data can reconstruct large-scale vegetation patterns despitemany pollen taxa having different ecological interpretations under different environmen-tal settings (Grabandt, 1980), representation of parent vegetation by pollen likely to be5

subject to inter-annual variability (Behling et al., 1997b), and tropical vegetation beingdifficult to reconstruct through pollen assemblages (Bush, 1991; Mancini, 1993; Bushand Rivera, 1998; Behling et al., 1997). These factors demonstrate the importanceof basing the input matrices for the biomisation process on all the available ecologicalinformation that allowing for the multiple assignment of the pollen taxa to the PFTs.10

4.1 Late Quaternary biome changes and palaeoenvironmental interpretations

4.2 6000±500 14C yr BP

Compared to the present, the sites at 6000±500 14C yr BP record either the samebiome or one indicating more xeric vegetation. Dry environmental conditions in south-ern Brazil extend from the early Holocene until approximately 4500 14C yr BP when15

there was an increase in arboreal taxa (Alexandre et al., 1999). Maximum aridity insoutheast Brazil was reached between approximately 6000 and 5000 14C yr BP, prior tothe transition to a modern climate (Behling, 1997a). The driest phase in central Brazilis at approximately 5000 14C yr BP; relatively moist climate conditions similar to todaysetting in after 4000 14C yr BP (Ledru, 1993; Marchant and Hooghiemstra, 2004). Al-20

though fire has been proposed as being responsible for late Holocene variation in theforest/savanna boundary in Brazil (Vernet et al., 1994; Desjardins et al., 1996; Horn,1993), this relative aridity is thought to reflect an extended dry season during this pe-riod (Behling, 1997b). An extended dry season may explain why Araucaria-dominatedforest were still restricted in their distribution relative to the modern day, not signifi-25

cantly increasing in range until approximately 3000 14C yr BP (Behling, 1997a). Fromour analysis the temporal perspective is missing, hence, we are unable to indicate if

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the vegetation reflects a stable dry period, or a period where there are alternating pe-riods of dry and humid climates linked for example to El Nino activity (Martin et al.,1993; Sifeddine et al., 2001). A relatively dry phase is also recorded in northwest-ern Argentina between 7500 and 5800 14C yr BP (Schabitz, 1991). Although pollenassemblages do not lend themselves well to distinguishing moisture from temperature5

changes, stable hydrogen isotope analysis on mosses show the vegetation of southernSouth America is highly sensitive to changes in moisture regime (Pendall et al., 2001).The predominance of steppe in southeastern Argentina agrees with the reconstructionby Prieto (1996): steppe characterising the area between 7000 and 5000 14C yr BP.Locally high moisture levels at sites closer to the Atlantic Ocean (Prieto, 1996) may10

explain why sites under strongest maritime influence (Aguads Emendadas, Cerro LaChina) changes from steppe to tropical dry forest as the local environment is able tosupport more arboreal taxa. In soutwestern Patagonia a sustained increase in Nothofa-gus pollen has been detected from around 6800 yr BP thought to result from locally in-creased moisture levels (Villa-Martinez and Moreno, 2007) Locally increased moisture15

levels in this part of Latin America during the mid Holocene are though to stem fromintensification of the southern Westerlies (Gilli et al., 2005).

Farther west, cool temperate rain forest assignments indicate a similar climaticregime and the maintenance of Valdivian rain forest (Villigran, 1988). A dry phaseis also recorded at many Andean sites, for example, in northern Chile desiccation20

of the Puna ecosystem is recorded between 8000 and 6500 14C yr BP (Baied andWheeler, 1993; Villigran, 1988). In lowland Chile, the period of maximum aridity oc-curred between 9400 and 7600 14C yr BP with drier than present conditions continu-ing until 5000 14C yr BP (Heusser, 1982), this could explain the increased presenceof steppe at sites along the southern Andes. On the central Peruvian Andes, a dry25

warm climate was encountered between 7000 and 4000 14C yr BP (Hansen, Seltzerand Wright, 1994). δ18O measurements from an ice core record taken from highlandPeru show that mid-Holocene climatic warming and drying was recorded from 8200to 5200 14C yr BP with maximum aridity between 6500 to 5200 14C yr BP (Thompson et

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al., 1995). Farther north on the Bolivian Andes, a dry phase is recorded from approxi-mately 5500 14C yr BP (Abbot et al., 1997). The slight increase in the number of arbo-real biome assignments at northern Andean sites can be interpreted as an up-slopeshift of forest line. This conforms to the suggestion based on pollen data by van Geeland van der Hammen (1973) that the vegetation zones in the northern Andes were5

several hundred of meters higher than the present at approximately 6000 14C yr BP.Relatively dry conditions have also been indicated for lowland Colombia for the mid-Holocene although the onset of dry conditions varied considerably between sites –occurring between 6500 and 4500 14C yr BP (Behling et al., 1999). Added complexityis caused by steep environmental gradients associated with non-climatic factors. For10

example, the presence of the tropical dry forest biome in lowland Colombia, e.g. thecatchment of El Pinal, results from a combination of strongly seasonal conditions atpresent and locally strong edaphic influence (Behling and Hooghiemstra, 1999).

Farther north, the assignment of Lake Valencia to the tropical dry forest is in agree-ment with the site-specific interpretation that more arboreal taxa (Bursera, Piper and15

Trema) were present after approximately 10 000 14C yr BP due to the onset of a morehumid climate (Bradbury et al., 1981): these tropical raingreen taxa indicative of a sea-sonal climate with relatively dry conditions. This appears to be a regional signal as earlyHolocene evergreen forests of northern Venezuela were replaced by semi-deciduouselements during the mid-Holocene (Leyden, 1984). Enhanced precipitation over Cen-20

tral America being accompanied by a northward shift of the ITCZ, enhanced southerliesand cooler equatorial sea surface temperatures (Harrison et al., 2003). Low lake levelsin central Panama also indicate that environmental conditions at this period were morexeric (Piperno et al., 1991b; Bush et al., 1992) whereas sites on the Yucatan peninsulashow a shift to warm evergreen forest where the warmer conditions that characterise25

the early Holocene persisted until approximately 6500 14C yr BP (Brown, 1985). Thisresult may stem from locally high moisture levels as a result of maritime influence,a similar mechanism having being proposed to explain a comparable shift in coastalBrazil and Argentina. Despite the majority of the evidence for a mid-Holocene dry pe-

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riod, there still remains a debate about the intensity, and even the occurrence, of this.Salgado-Labouriau et al. (1998) suggests that most savanna areas were characterisedby increased rainfall between 7000 and 6000 14C yr BP although there is considerablevariation in the timing of the onset of more humid conditions so it may be that such amesic period falls outside our temporal window.5

One of the main mechanisms used to explain moisture shifts is fluctuations in theSouthern Oscillation and the migration of the ITCZ (Martin et al., 1997). Martin etal. (1997) suggests that during the mid-Holocene, the ITCZ was located farther norththan its present-day position (Fig. 1) – this would produce a summer rainfall deficitand increased winter precipitation; in short greater seasonality. Rather than changes10

in the median position of the ITCZ, changes in the character of the ITCZ oscillation,such as greater latitudinal range for annual migration, can be invoked to explain vege-tation changes (Behling and Hooghiemstra, 2001). However, due to the topographicalinfluence of the Andes and the convergence of westerly and easterly winds, the ITCZhas a sinusoidal profile over northern South America (Fig. 1). Therefore, moisture15

changes over northeastern South America are likely to result from the importance ofconvective moisture sources; reduced precipitation, particularly in mid latitude west-ern South America, following reduced intensity of westerly climate systems. It is alsopossible that episodic dry events that presently occur in South America in relation tosea-surface temperature anomalies of the Pacific Ocean (ENSO) were more frequent20

in the mid-Holocene (Markgraf, 1998). This later suggestion may also have led to theincreased fire frequency indicated in southeast Brazil (Alexandre et al., 1999).

This regression of the forest during the mid-Holocene (8000 to 6000 14C yr BP) inthe southern tropical zone of Latin America is opposite to full forest development inAfrica (Servant et al., 1993; Jolly et al., 1998a) and this spatial relationship between25

Latin American and Africa warrants further investigation (Marchant and Hooghiemstra,2004). A particular target for the investigation could be the impact and feedbacksof vegetation changes on climate. For example, large changes in African vegetationabout the Sahel are suggested to have been important in influencing Indian monsoon

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dynamic (Doherty et al., 2000). Such a phenomena of vegetation feedbacks on theclimate system appears weaker in South America than in Africa although it is likelyto have had an impact as yet unqualified. Certainly Latin America would benefit fromtargeted model applications in the same way that has been applied to Africa (Kubatzkiand Claussen, 1998; Doherty et al., 2000). This modelling of climate dynamics Latin5

American represents a special challenge for climate models and modellers (Valdes,2000) primarily due to the steep environmental gradients and rapid transition from onebiome to another (Fig. 2).

4.2.1 18 000±100014C yr BP

The dating of the LGM in Latin America can be problematic (Bush et al., 1990;10

Hooghiemstra et al., 1992; Ledru et al., 1996, 1998; Sifeddine et al., 2001); Late Pleis-tocene sediments often containing sedimentary gaps at, or about, the LGM (Ledruet al., 1998), that are compounded by slow sedimentation rates. These sedimentaryconstraints make characterisation of the LGM vegetation highly contentious and havefuelled debates on LGM climates spanning two decades (Hooghiemstra and van der15

Hammen, 1998; Colinvaux et al., 2000; Thomas, 2000). Indeed, it has been suggestedthat some of the sites used in our analysis do not contain a sedimentary record of theLGM (Ledru et al., 1998) although due to application of a 2000 year-wide time window,we are able to include some of these sites with contentious sediments.

The LGM in Latin America, like most of the tropics, was characterised by a cold dry20

climate (Gonzalez et al., 2008). Ice caps were present on the southern tip of SouthAmerica which spread onto the plains and the coastal area (Heine, 1995). Evidencefrom glacial moraines also indicates considerable expansion of Andean glaciers (Hol-lis and Schilling, 1981; Villagran, 1988; Birkland et al., 1989; Seltzer, 1990; Thouretet al., 1997). Most of southern South America was characterised by an erosional25

environment; locations that would later accumulate sediments were glaciated, or sub-ject to fluvial activity (Heine, 2000). This situation is recorded by ice cores from thehigh Andes that contain large amounts of dust about the LGM, this being derived from

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surrounding deflating desert areas (Thompson et al., 1995). This cold, arid environ-ment is clearly reproduced by the vegetation which shows a transformation from thecool temperate rain forest to the cool grass/shrubland biome. Although Nothofagus-dominated forest is thought to have been extirpolated from coastal Chile at the LGM(Hollis and Schilling, 1981), fossil beetle assemblages in basal peat from Puerto Eden5

(49◦ S, 74◦ W) indicate that Nothofagus-dominated forest survived glaciation within theChilean channels (Ashworth et al., 1991). An earlier date of deglaciation of the TaitaoPeninsula indicates migration from Chiloe Island may explain the rapid re-growth ofNothofagus-dominated forest (Lumley and Switsur, 1993). Along the Chilean Pacificcoast the present cool evergreen forest was shifted approximately 5◦ northwards rela-10

tive to the present day; not as a discrete forest type but as a parkland type vegetationmosaic (Villagran, 1988), not forming closed forests until the early Holocene (Schabitz,1994; Heusser, 1995). This vegetation is evidenced with the analysis presented hereby the northernmost site (Laguna Six Minutes) recording cool temperate rain forest.However, it is unlikely this represents closed forest persisting in the area, trees be-15

ing present within a woodland/steppe vegetation mosaic (Villagran, 1988). The rate ofspreading of this forest into the Holocene would probably have been strongly depen-dent on the density of the parent plants from the initial seeding fraction (Huntingfordet al., 2000). The maintenance of cool temperate rain forest taxa, albeit at relativelylow levels, may result from high moisture levels as recorded by high lake stands at this20

time (Markgraf et al., 2000). These may reflect outbreaks of polar air and subsequentgeneration of low-pressure systems in the western Atlantic; combined with lower tem-peratures this situation would lead to a positive water balance. Indeed, the presence ofrelatively local moisture sources would have been important at the LGM and allow us toexplain regional patterns of biome change outside the influence of the ITCZ migrations25

(Markgraf et al., 2000).Considering the sites along the northern Andes, it is clear from the vegetation that

climate was colder during the LGM, reductions up to 12◦C may have been reachedat very high altitudes (Thompson et al., 1995). A substantial temperature depres-

399

sion during the last glacial period is mirrored by a significant impact on the vegetationcomposition and distribution. From our analysis it is apparent that the tree line wassignificantly lower at the LGM, concordant with a suggested lowering of vegetationzones by approximately 1000 to 1500 m relative to the present-day position (Monslave,1985; Wille et al., 2001). At lower elevations in western Colombia, a more conserva-5

tive depression of the vegetation has been suggested from Timbio (Wille et al., 2000).Indeed, the spatial character of the cooling and drying in the Neotropics is still underdebate (Markgraf, 1993; Colinvaux, 1996; Hooghiemstra and Van der Hammen, 1998;Farrera et al. 1999; Boom et al., 2002). Greater temperature change at high altitudescompared with those at low altitudes and at the sea surface (CLIMAP, 1976) can be ex-10

plained in terms of changes in lapse rate (Bush et al., 1990; Peyron et al., 2000; Willeet al., 2001) or compression of vegetation belts (Van der Hammen and Absy, 1994).The lapse-rate gradient is partly influenced by atmospheric moisture levels (Barry andChorley, 1990). As precipitation was reduced at the LGM, an overall steeper lapserate, particularly at higher altitudes where moisture reductions would have been high-15

est, seems likely (Wille et al., 2001). The extent to which lapse-rate changes can beused to explain spatially different signals from the data must be used with caution, par-ticularly as most palaeoclimatic reconstructions have been carried out with some kindof modern analogue-driven transfer function (Farrera et al., 1999). These reconstruc-tions commonly do not take into account non-climatic parameters which would impact20

on vegetation composition and distribution such as volcanic activity (Kuhry, 1988), fire(Cavelier et al., 1998; Rull, 1999), UV-B insolation (Flenley, 1998) or atmospheric com-position, in particular changing CO2 levels (Woodward and Bond, 2004). For example,concentrations of CO2 reduced to glacial levels (200 ppmV) have been shown to havea very significant impact on tropical vegetation (Jolly and Haxeltine, 1997; Boom et al.,25

2002; Marchant et al., 2002b).In south-east Brazil vegetation at the LGM was characterised by tropical dry forest

and tropical seasonal forest; this latter vegetation type may have been restricted withindeep valleys and along waterways; site-specific records from southeast Brazil indicate

400

open grasslands (campo limpo) with forest elements being retained as gallery forest(Behling, 1997a). Some model reconstructions of global vegetation patterns have indi-cated that there was an increase in warm evergreen forest in Brazil at the LGM at theexpense of tropical seasonal forest (Prentice et al., 1993). This pattern of change issupported by the data presented here where plants generally found at high altitudes to-5

day were more common in Amazonia at the LGM. Clapperton (1993) used geomorphicdata to infer a very sparsely vegetated landscape on the Brazilian Highlands, possiblyrelating to our reconstruction of steppe for a site in eastern Brazil. However, the coolgrass/shrubland biome appears to be a common type of vegetation at this time. Coldclimates in eastern South America could have resulted from the incursion of polar cold10

fronts that would occasionally reach northwards of the equator (Ledru, 1993; Behlingand Hooghiemstra, 2001). This phenomenon could combine with equatorward shiftof polar high-pressure areas and mid-latitude cyclones resulting in displacement andcompression of the subtropical anticyclone between mid latitudes westerlies (Dawson,1992). This climatic regime would result in more restricted migration of the ITCZ and15

pronounced aridity that would have been compounded by lower sea surface tempera-tures and associated reduction in atmospheric moisture.

At altitudes of approximately 3000 m in northern Peru vegetation about the LGMcomprised a mixture of wet and moist montane forest elements with open woodland(Hansen and Rodbell, 1995); this vegetation association having no modern analogue.20

Although the Andes remained relatively moist at the LGM, particularly in the northernpart where the concave shape of the mountain chain entrap moisture from the risingair (Fjeldsa et al., 1997), it is not certain what occurred in the lowland areas to the eastof the Andes (Colinvaux, 1989; Colinvaux et al., 1997; Bush et al., 1990; Thouret etal., 1997). In the Colombian lowlands two sites are characterised by the tropical dry25

forest biome, this agrees with the suggestion from a pollen study at Rondonia that veryopen savanna characterised the catchment at the LGM (van der Hammen and Absy,1994). Similarly, sparse vegetation cover would have been present on the Plateau ofMato Grosso (Servant et al., 1993) and is likely to have extended along the coastal

401

areas of Guyana and Surinam (Wijmstra, 1971) – this scenario is supported by ouranalysis, i.e. a site in lowland Panama recording tropical seasonal forest. Althoughthe majority of the area presently covered by drier types of tropical forest would prob-ably have been replaced by more open woodland at the LGM, environmental changesin savannas at the LGM appear to have been spatially complex. Whether the drier,5

cooler, conditions resulted in restricted range forest refugia cannot be answered fromthe available evidence although the vegetation appears heterogeneous as a mosaic ofAndean, savanna and tropical rain forest taxa combined. Indeed, this reiterates thesuggestion by Colinvaux et al. (2001), now widely accepted within the palaeoecolog-ical community, that plants responded to Quaternary climate changes as individuals10

not as biomes. Therefore, to fully investigate vegetation response to climate change isnecessary to retain information contained within the affinity scores to the sub-dominantbiomes (Marchant et al., 2002), or to carry out the analysis at the PFT level. Indeedthis approach would allow investigations into which elements of the vegetation wereparticularly sensitive to environmental change. Expansion of savanna could have been15

aided by reduced CO2 concentrations and the resultant competitive advantage attainedby C4 grasses over C3 plants (Haberle and Maslin, 1999; Marchant et al., 2002).

Within highland Mexico, warm mixed forest continues to be reconstructed due tothe presence of Pinus and Quercus-dominated forests. Although the same biome isrecorded at all these periods, it unlikely to be analogous to present day mixed forest;20

this was characterised by sparsely forested temperate scrub (Binford et al., 1987).Indeed, a strong aridity signal is directly recorded by low lake levels in central Mexicodue to reduced northern excursion of the ITCZ, trade wind circulation, and ensuingreduced oceanic-land moisture transfer (Markgraf et al., 2000) that would have beenreflected in ecosystem response. For example, forest on the Pacific side of the Central25

America contained a mosaic of high and low altitude forest species; a similarly noveltype of forest has also been shown for Mera, Ecuador (Liu and Colinvaux, 1985) andPeten, Guatemala (Leyden, 1984). Of the two sites that record the warm evergreenforest biome at this period a site in Guatemala was dominated by Chenopodiaceae,

402

Juniperus, Pinus and Quercus.We have presented vegetation reconstructions throughout Latin America at

6000±500 14C yr BP and 18 000±1000 14C yr BP using an objective method basedon biomes, constituent PFTs that are described by a set of unique pollen spectra.As a unified methodology has been applied to the pollen data, this reconstruction5

of biomes provides an objective basis for interpreting large-scale vegetation dynam-ics, and the environmental controls on these over the Late Quaternary and can beused as a dataset for model-data comparisons at 6000 and 18 000 yr BP. Changes at6000±500 14C yr BP, although relatively small, indicate a transition to more xeric veg-etation. The changes at 18 000±1000 14C yr BP are more homogenous and indicative10

of a cooler, drier climate. These reconstructions are consistent with numerous site-specific interpretations of the pollen data. The success of the reconstruction has inpart been determined by the coarse resolution of biome definitions, and using the mostdominant biome for description and interpretation of the results. To develop under-standing of vegetation response to environmental change, and possible feedbacks,15

information that is presently redundant should be retained and the results combinedwith climate/vegetation modelling initiatives. It is apparent from the relatively sparsecoverage, in comparison to Europe and North America, that the Late Quaternary vege-tation history of the Neotropical phytogeographical realm remains still relatively poorlyresolved despite its importance in model testing, developing biogeographical theory20

(Tuomisto and Ruokolainen, 1997), and understanding issues concerned with biodi-versity and human-environment interactions. It has been shown that environmentalchange is rarely spatially uniform and as such necessitates an even greater number ofsites to determine more precisely this complexity and the driving mechanisms behindthis. New sites, located in key areas, combined with the application of a range of prox-25

ies of environmental change, are required to refine our understanding of Neotropicalecosystem responses to Late Quaternary climatic variations.

403

Acknowledgements. This research has been funded by the Netherlands Organisation forScientific Research (NWO) under award 750:198:08 to Henry Hooghiemstra/Rob Marchant.Rob Marchant’s recent work to this paper was supported by EU Grant No: EU-MEXT-CT-2004-517098 and contributes to the Global Land Project (http://www.globallandproject.org).This paper contributes to the BIOME 6000-research initiative which is a community-wide col-5

laboration that started in 1994 under four elements of the International Geosphere-BiosphereProgram (IGBP-GAIM, IGBP-DIS, IGBP-GCTE and IGBP-PAGES) with the aim to create fullydocumented pollen and plant macrofossil data sets for 6000 and 18 000 14C yr BP. DominicJolly was instrumental in developing and applying these techniques in Africa and subsequentapplication to different geographical regions – indeed the Latin American application presented10

here would not have been possible without this massive contribution an encouragementthroughout the development of this work. In particular we thank Sandra Diaz, Liz Pickett,Colin Prentice and Bob Thompson for comments on earlier drafts of this manuscript. AryTeixeira de Oliveira-Filho and Olando Rangel are particularly thanked for comments on theecology of the Latin American taxa. Kirsten Sickel, Gerhard Boenish and Silvana Schott are15

thanked for help in producing the figures and archiving the results. Particular thanks must go toEric Grimm, John Keltner and Vera Markgraf for their energies in establishing, and developing,the Latin American Pollen Database. At the very centre of this work are all those palynologistswho have supplied data to the LAPD, or specifically to be used for this paper; without theirefforts such syntheses would not be possible.20

The publication of this article is financed by CNRS-INSU.

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Table 1. Characteristics of the sites from Latin America: specifically detailing location, potential vegetation aroundthe sites, sample type, age range of sediments, number of C14 dates, data type, principle analysts and associatedreferences. Dating control (DC) codes are based on the COHMAP dating control scheme (Webb, 1985; Yu andHarrison, 1995). For sites with a continuous sedimentation (indicated by C after the numerical code), the dating controlis based on bracketing dates as follows. 1: both dates within 2000 years of the selected interval, 2: one date within2000 years the other within 4000 years, 3: both dates within 4000 years, 4: one date within 4000 years one date within6000 years, 5: both dates within 6000 years, 6: one date within 6000 years the other within 8000 years, 7: bracketingdates more than 8000 years from the selected interval. For sites with discontinuos sedimentation (indicated by D afterthe numerical code), the dating control is based on single dates 1: indicated a date within 250 years of the selectedinterval, 2: a date within 500 years, 3: a date within 750 years, 4: a date within 1000 years, 5: a date within 1500 years,6: a date within 2000 years, 7: a date of more than 2000 years from the selected interval.

Code Biome Definition Main locations Equivalent Floristic characteristics

TRFO Tropical Closed canopy lowland Characterise much of Amazonian forest, Generally characterised byrainforest evergreen forests. the Amazon catchment. Can form tropical moist forest, plants with mesophyll leaf,

Canopy broken by a relatively thin band along Atlantic rain forest, although some sclerophyllousemergent trees (>40 m). tropical coastal areas, e.g. Atlantic terra firme forest, plants are present,MTCO >18◦C, rain forest of Brazil, Choco Varzea, Gallery forest, often tree ferns and palms.α>1500, pluvial forest of Colombia, Choco pluvial forestfrost-intolerant. maintained by high moisture

derived from close proximityof oceanic influence.

TSFO Tropical Relatively tall (20–30 m) Dominant to the north Marsh forests, A mix of mesophyllous andseasonal closed canopy forest with of Amazonian tropical savanna gallery forest, sclerophyllous taxa.forest occasionally tall (>40 m) rainforest, in central America Seasonal swamp forest The structure of the forest

emergent trees. and formerly extensive with palms. is dependent on moistureCanopy opens in a mosaic in the interior of Brazil demand and length of dry seasonas deciduous elements prior to extensive clearance. – this determines the amountloose leaves. Seasonally of deciduous taxa. Palmsdry from 1–4 months. can be locally common.

TDFO Tropical Relatively low (5–10 m), Extensive in central Brazil where Andean xerophytic bush, Xeromorphic characteristic,dry occasionally tall (20 m) it abuts tropical rainforest. Cerrado, Campo rupestres, dparticularly rought and fire tolerant.forest trees. Mixed forest, More fragmented in northwestern Campo cerrado (“campo” is For example, microphyllous leaves,

forming where the dry South America where a free draining more associated with thorns, deciduous leaves, thick bark,season leads to drought substrate leads to water-stress. grasslands). Cactus forest, stomata often present along lines.and plant water stress. Extends to mid altitudes, particularly Matorral, Deciduous xerophytic Drought adapted taxa are common,

rwithin ain shadow areas. Extensive forest, Andean xerophytic bush, e.g. tree cacti (Opuntiain western Central America and Espinar, Restinga dune Tforests, and Jasminocerus) withMexico. Present on the Galapagos horn forest, Chaco. dense undergrowth of shrubsIslands, extensive in Chile, central and herbs.South America. Associated withthe Andes, particularly within rainshadow areas. Extends into dryareas of Central Americasuch as the Yucatan peninsular.

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Table 1. Continued.

Code Biome Definition Main locations Equivalent Floristic characteristics

WTRF Warm Evergreen closed forest, Extending along the Andes Lower montane forest, A mix of mesophylloustemperate of relatively low stature (<20 m) at mid to low elevations (500–2000 m). moist lower montane forest, and sclerophyllous taxarainforest with tall emergent trees (>25 m). Present at slightly lower elevations submontane forest, constrained by altitude

Not tolerant of freezing. in eastern Brazilian highlands (<1000 m). subAndean forest, and length of dryA transitional forest type The similarity with the Andean Araucaria-dominated forest season. Palms and treebetween lowland and higher flora indicates these areas have also with Podocarpus. ferns can be locally common.altitude forms (1000-2500 m). been connected in the past

WEFO Warm Evergreen semi-closed Present within a relatively Andean forest, A mix of mesophylloustemperate forest with tall restricted range transitional Andean forest, and sclerophyllous taxa.evergreen emergent trees (>30 m). and along the Andes, upper Andean forest Tree ferns can bebroadleaf Not tolerant of freezing. particularly present locally common.forest from 1000–2000 m.

CTRF Cool Medium height (<15 m) Predominant along western Patagonian rain forest, A mix of mesophylloustemperate closed canopy forest coast of southern temperate rain forest, and sclerophyllous taxa.rainforest with a dense under-story. South America extending valdivian rain forest, The structure can be

Can tolerate freezing. to Patagonian steppe. magallenic rain forest quite variable dependingAlso present along the Andes on location – fromat mid to high altitudes. dense forest to scrubby heath.

WAMF Warm Medium height (<15 m) Mid to high altitudes Pinus and Mixed evergreen foresttemperate open canopy with open of north Central Quercus-dominated dominated by sclerophyllousmixed under-story. Drought America, in particular forest. taxa that requireforest tolerant, semi fire-tolerant. Mexico warm for bud-burst.

COMI Cool Short stature woodlands (<5 m) High Andean shrub/dwarf Upper montane forests, Predominantly evergreenmixed open canopy, open under-story tree forests, present close high Andean forest, taxa with physiologicalforest forest. Frost tolerant. to the forest line cloud forest adaptation to night frost,

e.g. retaining old leavesfor insulation.

CGSS Cool Common above the forest Present only at Puna, Heath, Poaceae-dominated coolgrasslands line of the Andes, the highest altitudes Cushion heath grasslands with occasional

dominated by tussock of the Andes cushion plantsgrasses and cusion plants.

STEP Steppe Dominated by grasses, Extensive in eastern Argentina, Steppe grasslands, Grasses and chenopodsoccasional shrubs and present in lowland Central America Campo limpo, forming low altitudesteppe herbs. Profuse and northeast Brazil Pampa warm grasslandsflowering duringthe wet season

DESE Desert Open semi-arid Coastal Peru and Chile and Coastal desert Occurrence of CAM-plants,to arid vegetation western Mexico, the former cacti and succulents

area due to rain-shadowfrom the Andes

CGSH Cool Tropic-alpine environments, Present from extreme southern Paramo, Subparamo, Poaceae-dominated coolgrass common above the forest line South America, on Tierre Magallenic moorland, grasslands with numerousshrublands of the Andes. A mixture del Fuego above the forest line Paramillo, Vegas tussock forming grass.

of tussock grasses and of the Andes (2800–4000 m). Also present are shrubs,cold-adapted shrubs. e.g. Empetrum, Espeletia

and Puya.

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Table 2. Range of plant functional types identified within the Latin American region givingbioclimatic range and physiological adaptation.

Code Plant functional type Bioclimatic range and plant physiological adaption

g Graminoid Ecologically broad category that occurs in a number of biomes, a highlyadaptive PFT with a ubiquitous distribution and little diagnostic value.

man Mangrove Constituent of lowland tropical vegetation, control on distribution is mainlyhydrological

tx Tree fern Can be locally dominant. Occupying a broad bioclimatic range, recordedin a range of moistenvironments from lowland to montane, particularly common in temperate areas.

Te1 Tropical mesic drought-deciduous broad-leaved tree MTCO>15.5◦C, α>0.7, short dry season (1 month), GDD>5000Te2 Tropical xeric drought-deciduous broad-leaved tree MTCO>15.5◦C, α>0.6–08, longer dry season (2–4 months),

GDD>5000, withstands longer dry season by shedding leavesTr1 Tropical evergreen broad-leaved tree MTCO>15.5◦C, α>0.9, GDD>5000, present in wettest tropical rain forest.Tr2 Tropical mesic evergreen broad-leaved tree MTCO>15.5◦C, α 0.8–0.9, GDD>5000, present in range of tropical

seasonal forest typesctc Warm temperate evergreen needle-leaved tree MTCO5◦C—15◦C, α>0.7, GDD>4500, common in the Brazilian highlandsctc1 Cool temperate evergreen needle-leaved tree MTCO −5◦C—10◦C, α 0.95–0.75, GDD>900, common along the western coast

of southern South Americactc2 Cold evergreen needle-leaved tree MTCO −10◦C—5◦C, α>0.65, GDD>1000, common along the western coast

of southern South Americaec Eurythermic conifer MTCO>5◦C, α 0.4–0.6, GDD>5000, common within dry forest

of South America and Mexicotxts Drought-tolerant small-leaved low or high shrub MTCO>20◦C, α 0.2–0.35, GDD>5000, woody shrubs common in dry forestds Desert shrubs MTCO>20◦C, α 0.2–0.35, GDD>5000, woody shrub and cacti in Mexico

and coastal Perudf Eurythermic drought-adapted forb MTCO>20◦C, α 0.2–0.35, GDD>5000, woody shrub and cacti in Mexico

and coastal Perutf Tropical drought-intolerant forb MTCO>15.5◦C, α>0.6, GDD>5000, frost intoleranttef Temperate drought-intolerant forb MTCO>5◦C–15◦C, α>0.6, GDD>1000, frost tolerantsf Eurythermic drought-tolerant forb MTCO 5◦C–10◦C, α 0.65–0.7, GDD 2500–4000, requires

a seasonal moist environmentaf Arctic forb MTCO −5◦C–0◦C, α 0.05–0.1, GDD<500, frost tolerantcp Rosette or cushion forb MTCO< − 5◦C, α<0.2, GDD<500, specific growth form, frost tolerant.wte Warm temperate evergreen broad-leaved tree MTCO 5◦C–15◦C, α>0.65, GDD>3000, frost tolerant mesophyllous treests Temperate evergreen broad-leaved tree MTCO 0◦C–5◦C, α>0.65, GDD>2000, frost tolerant micro and mesophyllous treests1 Temperate evergreen sclerophyll broad-leaved tree MTCO −5◦C–5◦C, α>0.5, GDD>1000, sclerophyllous, usually evergreenwte1 Temperate (spring-frost avoiding) cold-deciduous MTCO 0◦C–15◦C, α>0.6, GDD>3000, winter deciduous, requires

broad-leaved tree warm growing season.wte4 Temperate (spring-frost tolerant) cold-deciduous MTCO −5◦C–5◦C, α 0.55, GDD>2500, winter deciduous, requires warm

broad-leaved tree growing season but this can be short.aa Arctic evergreen broad-leaved erect dwarf shrub MTCO −5◦C–0◦C, α 0.2–0.4, GDD 500–1000, frost tolerant

430

Table 3. Biomes identified within the Latin American region as portrayed in the vegetation map (Fig. 2) indicting afloristic description, the main location and equivalent floristic units found in a macro scale analysis of the Latin Americanvegetation.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

Lake Asa 3 Antarctica −61.13 −62.62 35 1280–4980 CGSH Soil 14 - - Raw Bjorck, S. Bjorck et al. (1993)SalinaAnzotegui Argentina −63.77 −39.06 −5 0–10 360 STEP Playa 5 3D - Raw Schabitz, F. Schabitz (1994)Patagonia Argentina −68.10 −50.00 0 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)La Mision Argentina −67.83 −53.5 5 0–990 STEP Mire 4 4C - Raw Markgraf, V. Markgraf (1993)Patagonia Argentina −72.98 −50.15 20 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.90 −50.20 20 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −68.30 −50.00 20 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Harberton Argentina −67.16 −54.88 20 0–13 360 STEP Mire 16 1C - Raw Markgraf, V. Markgraf (1989,

1991, 1993)Ruta 3.4 Argentina −62.79 −40.50 20 Modern STEP Soil - - - Raw Schabitz, F. Schabitz (1994)Ruta 3.3 Argentina −62.59 −40.08 20 Modern STEP Soil - - - Raw Schabitz, F. Schabitz (1994)Pedro Luro Argentina −62.53 −39.50 20 Modern STEP Soil - - - Raw Schabitz, F. Schabitz (1994)Origone Argentina −62.43 −39.08 20 Modern STEP Soil - - - Raw Schabitz, F. Schabitz (1994)Patagonia Argentina −72.95 −50.15 50 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.90 −50.15 50 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.90 −50.25 50 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.90 −50.05 50 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −68.60 −50.05 50 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Espuma Argentina −63.25 −40.67 50 Modern TDFO Soil - - - Raw Schabitz, F. Schabitz (1994)Patagonia Argentina −72.85 −50.15 60 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.75 −50.15 60 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.80 −50.15 70 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.70 −50.20 80 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)ArroyoSauce Chico Argentina −62.23 −38.07 85 Modern STEP Soil - - - Raw Prieto, A.R. Prieto (1996)Gaviotas Argentina −63.65 −39.07 90 Modern TDFO Soil - - - Raw Schabitz, F. Schabitz (1994)Patagonia Argentina −72.55 −50.10 100 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −68.90 −50.05 100 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)EmpalmeQuerandıes Argentina −60.65 −37.00 105 0–15 000 STEP Lake 8 2C - Raw Prieto, A. R. Prieto (1996)Ruta 250.19 Argentina −65.58 −39.54 117 Modern STEP Soil - - - Raw Schabitz, F. Schabitz (1994)Patagonia Argentina −72.00 −50.15 150 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −72.00 −50.05 150 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −71.95 −50.10 180 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −71.70 −50.15 180 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −71.50 −50.15 180 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −71.10 −50.15 180 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −69.90 −50.15 180 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −69.30 −50.15 180 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −71.30 −50.15 190 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −70.90 −50.15 190 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −70.20 −50.15 190 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)

431

Table 3. Continued.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

Patagonia Argentina −69.60 −50.15 190 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)Patagonia Argentina −69.00 −50.10 190 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)MorenoGlacier Bog Argentina −73.00 −50.46 200 0–9550 CTRF Mire 2 4C - Raw Ager, T. A. Mercer and Ager (1983)Patagonia Argentina −70.50 −50.15 200 Modern CTRF Soil - - - Digi Mancini, M. V. Mancini (1993)CerroLa China Argentina −58.64 −37.84 200 0–10 500 STEP Soil 2 4C - Raw Prieto, A. R. Prieto and Paez (1989),

Paez and Prieto (1993)LagoBsAs Argentina −71.45 −46.44 230 Modern CTRF Soil - - - Raw Schabitz, F. Schabitz (1994)Pico Salam Argentina −67.43 −45.42 637 Modern STEP Soil - - - Raw Schabitz, F. Schabitz (1994)AlercesNor Argentina −71.60 −42.56 800 Modern CTRF Soil - - - Raw Schabitz, F. Schabitz (1994)Mallin Book Argentina −71.58 −41.33 800 290–14 200 CTRF Mire 9 1C - Raw Markgraf, V. Markgraf (1983)Primavera Argentina −71.18 −40.66 800 1800–4380 CTRF Midden 6 - - Raw Markgraf, V. Markgraf et al. (in press )Comallo Argentina −70.21 −41.01 815 Modern CGSH Soil - - - Raw Schabitz, F. Schabitz (1994)Encantado Argentina −71.13 −40.66 960 290–1260 CTRF Midden 3 - - Raw Markgraf, V. Markgraf et al. ( in press )MesetaLatorre 1 Argentina −72.05 −51.52 980 240–7120 CGSH Mire 3 4C - Raw Schabitz, F Schabitz (1991)MesetaLatorre 2 Argentina −72.03 −51.44 1000 0–7000 CGSH Mire 1 6D - Raw Schabitz, F. Schabitz (1991)Cueva Haichol Argentina −70.66 −38.58 1050 200–6890 STEP Cave 2 1C - Raw Markgraf, V. Markgraf (1988)AustroEsqu Argentina −71.47 −42.66 1100 Modern CTRF Soil - - - Raw Schabitz, F. Schabitz (1994)Vaca Lauquen Argentina −71.08 −36.83 1450 0–11 260 CTRF Mire 3 1C - Raw Markgraf, V. Markgraf (1987)VeranadaPelan Argentina −70.38 −36.88 1860 0–10 890 CGSH Mire 3 3C - Raw Schabitz, F. Schabitz (1989)Salina 2 Argentina −69.33 −32.25 2000 100–6510 CGSH Mire 2 1C - Raw Markgraf, V. Markgraf (1983)VeranadaVulkanpickel Argentina −70.41 −36.68 2800 0–7790 CGSH Mire 1 7D - Raw Schabitz, F. Schabitz (1989)Salado Argentina −69.75 −35.33 3200 20–4330 CGSH Mire 2 - - Raw Markgraf, V. Markgraf (1983)Aguilar Argentina −65.75 −23.83 4000 0–9830 CGSH Mire 3 2C - Raw Markgraf, V. Markgraf (1985)LagunaBella Vista Bolivia −61.56 −13.58 750 0–55 000 TSFO Lake 15 2C 1D Digi Mayle, F. Mayle et al. (2000)Laguna Chaplin Bolivia −61.05 −14.50 750 0–40 000 TSFO Lake 14 2C 1D Digi Mayle, F. Mayle et al. (2000)Wasa Mayu Bolivia −65.91 −17.54 2720 1000–31 000 COMI Lake 1 7D 7D Raw Graf, K. Graf (1992)Lake Huina-mimarca Bolivia −69.00 −16.50 3765 0–25 000 CSGH Lake 48 1C 3C Digi Mourguiart, P. H. Mourguiart et al. (1995),

Argollo and Mourguiart (2000)Cerro Calvario Bolivia −68.50 −16.50 3950 0–8360 CGSH Mire 4 1C - Raw Graf, K. Graf (1992)Amarete Bolivia −68.98 −15.23 4000 0–9160 CGSH Mire 2 5D - Raw Graf, K. Graf (1992)Rio Kaluyo Bolivia −68.13 −16.43 4070 130–9920 CGSH Lake 3 3C - Raw Graf, K. Graf (1992)Sajama Bolivia −68.88 −18.16 4250 0–4400 CGSH Lake 9 - - Raw Graf, K. Graf (1992)Cotapampa Bolivia −69.11 −15.21 4450 0–9560 CGSH Mire 5 2C - Raw Graf, K. Graf (1992)CumreUnduavi Bolivia −68.03 −16.33 4620 0–9200 CGSH Mire 6 3C - Raw Graf, K. Graf (1992)Chacaltaya 1 Bolivia −68.13 −16.36 4750 80–7400 CGSH Mire 1 1C - Raw Graf, K. Graf (1992)Mt. Blanco Bolivia −67.35 −17.02 4780 1250–7500 CGSH Lake 7 1C - Raw Graf, K. Graf (1992)Katantica Bolivia −69.18 −14.8 4820 50–7720 CGSH Mire 3 1C - Raw Graf, K. Graf (1992)ReservaVolta Velha Brazil −48.38 −26.04 0 Modern WTRF Trap - - - Digi Behling, H. Behling et al. (1997)Lago Crispim Brazil −48.00 −0.8 0 0–9000 TRFO Lake 4 1C - Raw Behling, H. Behling et al. (1997)ODP site 932 Brazil −47.03 5.18 0 0–45 000 TRFO Fan 2 2C 1D Raw Behling, H. Haberle and Maslin (1999)Lagoa da Caco Brazil −43.43 −22.97 5 3000–20 000 TDFO Lake 14 1C - Raw Behling, H. Ledru et al. (2001)Poco Grande Brazil −48.86 −26.41 10 0–4840 WTRF Section 4 - - Raw Behling, H. Behling (2000),

Behling (1997a, b)

432

Table 3. Continued.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

Lagoa daCuruca 2 Brazil −47.85 −0.76 35 0–9440 TRFO Lake 4 2C - Raw Behling, H. Behling (2000)Rio (unclear) Brazil −38.00 −5.50 50 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)Rio Jaguaribe II Brazil −37.76 −4.55 50 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)Rio Jaguaribe I Brazil −37.75 −4.43 50 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)Picos Brazil −41.40 −7.06 70 Modern TDFO Soil - 1D 1D Raw Harbele, S. Behling et al. (2000)CampinaGrande I Brazil −35.75 −7.23 70 Modern TSFO Soil - - - Raw Behling, H. Behling et al. (2000)Rio Mirim Brazil −35.40 −5.64 70 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)Mirim Brazil −35.30 −5.68 70 Modern TDFO Soil - 1D 7D Raw Ledru, M.-P. Behling et al. (2000)Lagoa Grande Brazil −47.45 −7.08 75 Modern TDFO Lake - - - Raw Behling, H. Behling et al. (2000)Rio de Contas Brazil −39.00 −14.28 80 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)RioJequitinhonha Brazil −38.92 −15.85 80 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)Rio SaoFrancisco Brazil −36.50 −10.26 80 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)Rio Protengi Brazil −35.25 −5.78 80 Modern TDFO River - - - Raw Behling, H. Behling et al. (2000)Lago Bolim Brazil −35.18 −6.04 90 Moden TDFO Lake - - - Raw Behling, H. Behling et al. (2000)Comprida Brazil −53.15 −1.5 130 0–7200 TRFO Lake 5 1C - Digi Bush, M. Bush et al. (2000)Geral Brazil −53.00 −1.5 130 0–6000 TRFO Lake 2 1C - Digi Bush, M. Bush et al. (2000)Carajas Brazil −48.00 −5.00 150 0–20 000 TSFO Lake 8 2C 6C Digi Absy, M. L. Absy et al. (1991)Crominia Brazil −49.45 −17.28 200 0–32 200 TDFO Palm 5 2C 1D Digi Salgado- Salgado-Labouriau

swamp -Labouriau, M. L. et al. (1998)Atlantic Brazil −48.35 −25.95 200 Modern WTRF Trap - - - Raw Behling, H. Behling et al. (1997)AguadsEmendadas Brazil −47.58 −15.56 200 0–28 000 TDFO Palm 6 7C 7D Digi Salgado- Salgado-Labouriau

swamp -Labouriau, M. L. et al. (1998)Lagoa das Patas Brazil −66.68 0.26 300 0–42 210 TFFO Lake 16 1D 1C Raw De Oliveira, P. E. De Oliveira (1992)La Pata Brazil −66.66 0.25 300 0–45 000 TRFO Lake 12 2C 1D Digi Colinvaux, P. Colinvaux et al. (1996, 2000)Cuiaba Brazil −55.86 −15.35 350 Modern TDFO Soil - - - Raw Behling, H. Behling (in prep )Lago do Pires Brazil −42.21 −17.95 390 0–9720 TSFO Lake 7 1C - Raw Behling, H. Behling (1993), Behling (1997a, b)Rio SaoFrancisco Brazil −43.00 −10.46 400 0–11 500 TDFO River 6 1C - Digi De Oliveira, P. E. De Oliveira et al. (1999)Saquinho Brazil −43.23 −10.44 480 0–11 000 TSFO Mire 6 1C - Digi De Oliveira, P. E. De Oliveira et al. (1999)Assis Brazil −50.50 −22.68 540 Modern TSFO Soil - - - Raw Behling, H. Behling (in prep )Bauru Brazil −49.07 −22.32 570 Modern TSFO Soil - - - Raw Behling, H. Behling (in prep )Lagoa Santa Brazil −47.45 −22.36 630 Modern WTRF River - - - Digi Parizzi, M.G. Salgado-Labouriau et al. (1998)Brotas Brazil −48.08 −22.29 700 Modern WTRF Soil - - - Raw Behling, H. Behling (in prep )Botucatu Brazil −48.00 −23.00 700 Modern WTRF Soil - - - Raw Behling, H. Behling (in prep )Curcuab Brazil −48.00 −23.00 700 Modern WTRF Soil - - - Raw Behling, H. Behling (in prep )Katira Brazil −63.00 −9.00 750 0–60 000 TDFO Lake 4 7D 1D Digi van der Hammen, T. van der Hammen and Absy (1994)Rio da Curua Brazil −48.83 −23.83 800 0–7500 WTRF Lake 4 - - Raw Behling, H. Behling et al. (1997)Colombo Brazil −49.23 −25.33 920 Modern TSFO Trap - - - Raw Behling, H. Behling et al. (1997)Brasilia 1 Brazil −47.66 −15.59 1030 Modern TDFO Soil - - - Raw Behling, H. Behling ( in prep )Salitre Brazil −46.78 −19.00 1050 0–50 000 WTRF Lake 14 1C 7C Raw Ledru, M.-P. Ledru (1992, 1993),

Ledru et al. (1994, 1996)Serra daBoa Vista Brazil −49.15 −27.70 1160 0–14 000 WTRF Mire 4 2C - Raw Behling, H. Behling (1993), Behling (1997a, b)Serra CamposGerais Brazil −50.21 −24.66 1200 0–12 480 WTRF Mire 4 3C - Raw Behling, H. Behling (1997a)Serra do RioRastro Brazil −49.55 −28.55 1420 800–11 180 WTRF Mire 3 2C - Raw Behling, H. Behling (1993),

Behling (1997a, b)

Morro daIgreja Brazil −49.86 −28.18 1800 0–10 390 WTRF Mire 2 - - Raw Behling, H. Behling (1993), Behling (1997a, b)

433

Table 3. Continued.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

Morro deItapeva Brazil −45.63 −22.78 1850 0–35 010 WTRF Lake 9 4C - Raw Behling, H. Behling (1997b)Puerto delHambre Chile −70.92 −53.59 5 0–16 000 CTRF Mire 5 4C 7D Digi Heusser, C. Heusser et al. (1995)Laguna Lofel Chile −74.43 −44.85 50 0–16 500 CTRF Lake 5 2C 6D Digi Bennet, K. Bennet et al. (2000)Laguna Stibnite Chile −74.43 −46.43 50 0–14 000 CTRF Lake 6 1C 4D Digi Lumley, S. Lumley and Switsur (1993)Puerto Eden Chile −74.41 −49.13 50 0–14 750 CTRF Mire 7 4C - Raw Markgraf, V. Ashworth and

Markgraf (1989),Ashworth et al. (1991)

Laguna Stibnite Chile −74.38 −46.43 50 0–17 500 CTRF Lake 10 1C 2D Digi Bennet, K. Bennet et al. (2000)LagunaSix Minutes Chile −74.33 −46.43 50 0–17 500 CTRF Lake 4 4C 7D Digi Bennet, K. Bennet et al. (2000)Laguna Lincoln Chile −74.07 −45.34 50 0–16 500 CTRF Lake 5 2C 7D Digi Bennet, K. Bennet et al. (2000)Dichan Chile −73.88 −49.66 50 0–8000 CTRF Mire 5 2C - Digi Heusser, C. Heusser et al. (1995)EsteroHuitanque Chile −73.82 −43.61 52 0–14 000 CTRF Mire 9 1C - Digi Heusser, C. Heusser et al. (1995)Rano RarakuBore 3 Chile −109.28 −27.16 75 0–35 260 TDFO Lake 10 4C 4C Raw Flenley, J. Flenley and King (1984),

Flenley et al. (1991)Mayol Chile −73.75 −42.64 75 0–14 000 CTRF Mire 12 3C - Digi Heusser, C. Heusser et al. (1995)Punta Arenas Chile −70.97 −53.15 75 0–16 500 CTRF Mire 5 4C 7D Digi Heusser, C. Heusser et al. (1995)Torres del Paine Chile −72.66 −50.98 100 0–11 000 CTRF Lake 8 2C - Digi Heusser, C. Heusser et al. (1995)Rano Kao Chile −109.43 −27.18 110 0–1360 TDFO Lake 2 - - Raw Flenley, J. Flenley and King (1984),

Flenley et al. (1991)Puchilco Chile −73.62 −42.63 110 0–12 500 CTRF Mire 7 2C - Digi Heusser, C. Heusser et al. (1995)PuertoOctay PM13 Chile −72.90 −40.93 120 500–20 000 CTRF Mire 16 4C 1C Raw Moreno, P. I. Moreno (1994)Chepu Chile −73.66 −42.17 140 Modern CTRF Mire - - - Raw Moar, N. T. Godley and Moar (1973)La Esperanza Chile −72.83 −46.63 330 0–1500 CTRF Mire - - - Raw Graf, K. Graf (1992)Rano Aroui Chile −109.40 −27.08 425 0–37 600 TDFO Lake 11 2C 6C Raw Flenley, J. Flenley and King (1984)Caunahue Chile −72.00 −40.00 500 4370–14 930 CTRF Section 9 2C - Raw Markgraf, V. Markgraf (1991)San Pedro Chile −73.95 −42.25 650 Modern CTRF Mire - - - Raw Moar, N. T. Godley and Moar (1973)Tumbre 2 Chile −67.78 −23.31 3880 241–7500 CGSH Lake 3 2C - Raw Graf, K. Graf (1992)AguasCalientas Chile −67.42 −23.08 4210 0–6400 CGSH Mire 1 7D - Raw Graf, K. Graf (1992)Ajata Chile −69.20 −18.25 4700 0–1460 CGSH Mire 1 - - Raw Graf, K. Graf (1992)Boca deLopez Colombia −75.36 10.85 0 0–4000 TRFO Coastal 5 - - Raw van der Hammen, T. Behling, Berrio

and Hooghiemstra (1999)Jotaordo Colombia −76.66 5.66 50 0–4200 TRFO Lake 7 - - Raw Berrio, J. C. B. Berrio, Behling

and Hooghiemstra (2000)El Caimito Colombia −76.60 2.53 50 0–4500 TRFO Lake 4 - - Raw Wille, M. Wille et al. (1999)Monica-1 Colombia −72.50 −0.60 160 0–12 000 TRFO Lake 3 2C - Raw Behling, H. Behling, Berrio

and Hooghiemstra (1999)Mariname-II Colombia −72.03 −0.66 160 0–5000 TRFO Lake 5 1C - Raw Behling, H. Behling, Berrio

and Hooghiemstra (1999)Carimagua Colombia −74.14 4.04 180 0–8270 TDFO Lake 6 2C - Raw Behling, H. Behling and Hooghiemstra (1999)Sardinas Colombia −69.45 4.95 180 0–11 600 TDFO Lake 6 2C - Raw Behling, H. Behling and Hooghiemstra (1998)El Pinal Colombia −70.40 4.09 185 0–19 000 TDFO Lake 8 4C 2D Raw Behling, H. Behling and Hooghiemstra (1999)Piusbi Colombia −77.89 1.66 200 0–10 400 TRFO Lake 3 1C - Raw Behling, H. Behling and Hooghiemstra (1999)LagunaAngel Colombia −70.54 4.45 205 0–10 026 TDFO Lake 8 2C - Raw Behling, H. Behling and Hooghiemstra (1998)LagoAgua Sucia Colombia −73.54 3.46 260 0–15 340 TDFO Lake 4 7D - Raw Wijmstra, T. A. Wijmstra and

van der Hammen (1966)

434

Table 3. Continued.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

Loma Linda Colombia −73.35 3.22 310 0–8720 TDFO Lake 8 1C - Raw Behling, H. Behling, Berrioand Hooghiemstra (1999)

Pitalito Colombia −76.50 1.75 1300 0–7350 WEFO Mire 6 6D - Raw Bakker, J. G. M. van der Hammen et al. (1980)Piagua Colombia −76.50 2.30 1700 0–14 000 WEFO Lake 7 7D - Raw Wille, M. Wille et al. (2001),

van der Hammen et al. (1980)Pantano deGenagra Colombia −76.50 2.50 1750 0–9000 WEFO Mire 7 4C - Raw Behling, H. Behling, Negret and Hooghiemstra (1999),

van der Hammen et al. (1980)Rio Timbio Colombia −76.50 2.50 1750 0–14 000 WEFO Lake 6 2C - Raw Wille, M. van der Hammen et al. (1980)Libano Colombia −75.50 4.50 1820 0–14 000 WEFO Soil 1 7D - Raw Salomons, J. B. van der Hammen et al. (1980)de PedroPalo III Colombia −74.41 4.50 2000 0–5500 COMI Lake 2 7D - Raw van der Hammen, T. van der Hammen (1974)Herrera Colombia −73.91 5.00 2000 0–20 000 COMI Lake 3 4D - Raw van Geel, B. van Geel and

van der Hammen (1973)Ubaque Colombia −73.55 4.33 2000 Modern WEFO Lake - - - Raw Jean-Jarcob, K. Wille et al. (2001),

van der Hammen et al. (1980)CiudadUniversi-taria X Colombia −74.18 −4.75 2560 0−>35 000 COMI Lake 4 4C 7D Raw van der Hammen, T. van der Hammen and Gonzalez (1960)El Abra II Colombia −73.96 5.02 2570 0–11 000 COMI Cave 1 7D - Raw Schreve-Brinkman, E. J. Schreve-Brinkman (1978)Fuquene II Colombia −73.87 5.50 2580 0–25 000 COMI Lake 2 7D 7C Raw van Geel, B. van Geel and

van der Hammen (1973)Alsacia Colombia −74.11 4.09 3100 0–13 700 COMI Mire 3 6D - Raw Melief, A. B. M. Melief (1985)AguaBlanca Colombia −74.45 5.0 3250 0–46 000 COMI Mire 2 6D 7D Raw Kuhry, P. Graf (1992), Kuhry (1988b),

Kuhry et al. (1983)Cienaga delVisitador Colombia −72.83 6.13 3300 0–12 000 COMI Mire 2 7D - Raw van der Hammen, T. van der Hammen and Gonzalez (1965)

La Guitarra Colombia −74.28 4.00 3450 0–15 300 COMI Mire 3 4C - Raw Melief, A. B. M. Melief (1985)Ciega I Colombia −72.31 6.50 3510 0–2000 COMI Lake 1 - - Raw van der Hammen, T. van der Hammen et al. (1980)La Primavera Colombia −74.13 4.00 3525 0–11 200 CGSH Mire 6 1C - Raw Melief, A. B. M. Melief (1985)de la America Colombia −74.00 4.33 3550 0–9000 CGSH Mire 1 1D - Raw Kuhry, P. Kuhry (1988), van der Hammen and

Gonzalez (1960)ParamoPalacio Colombia −73.88 4.76 3550 0–5500 CGSH Mire 4 5D - Raw van der Hammen, T. van der Hammen and

Gonzalez (1960)Andabobos Colombia −74.15 4.09 3570 0–15 000 CGSH Mire 2 7D - Raw Melief, A. B. M. Melief (1985)Paramo dePena Negra Colombia −74.09 5.09 3625 0–12 500 CGSH Mire 10 2C - Raw Kuhry, P. Kuhry et al. (1983)Paramo deLagunaVerde Colombia −74.00 5.25 3625 0–5500 CGSH Mire 2 4D - Raw Kuhry, P. Kuhry et al. (1983)El Bosque Colombia −75.45 8.85 3650 0–4700 CGSH Mire 4 - - Raw Melief, A. B. M. Kuhry (1988a), Melief (1985)Turbera deCalostros Colombia −73.48 4.41 3730 Modern CGSH Soil 1 - - Raw Salomons, J. B. van der Hammen et al. (1980)Bobos Colombia −72.85 6.13 3800 0–5000 CGSH Lake 4 6D - Raw van der Hammen, T. van der Hammen (1962)El Gobernador Colombia −75.00 3.95 3815 0–10 050 CTRF Mire 2 2C - Raw Melief, A. B. M. Melief (1985)Valle deLagunillas Colombia −72.34 6.50 3880 0–7100 CGSH Lake 8 7D - Raw van der Hammen, T. van der Hammen et al. (1980)La Rabona Colombia −74.25 4.05 4000 0–5100 CGSH Mire 1 4D - Raw Melief, A. B. M. Melief (1985)Greja Colombia −73.70 4.86 4000 0–12 000 CGSH Lake 2 3C - Raw van der Hammen, T. van der Hammen (1962)CorazonPartido Colombia −74.25 4.00 4100 Modern CGSH Mire - - - Raw Melief, A. B. M. Melief (1985)El Trinagulo Colombia −74.25 4.00 4100 Modern CGSH Mire - - - Raw Melief, A. B. M. Melief (1985)Santa Rosa1 Costa Rica −85.66 10.84 0 Modern TRFO Soil - - - Digi Horn, S. P. Rodgers and Horn (1996)Santa Rosa2 Costa Rica −85.64 10.83 0 Modern TSFO Soil - - - Digi Horn, S. P. Rodgers and Horn (1996)

435

Table 3. Continued.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

CararaBiological Reserve Costa Rica −84.62 9.73 0 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)Tortuguero Costa Rica −83.53 10.53 0 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)Carara Costa Rica −84.60 9.88 35 Modern TSFO Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)CantarranaSwamp Costa Rica −84.00 10.45 36 Modern TRFO Swamp - - - Digi Horn, S. P. Rodgers and Horn (1996)SendroSedro Swamp Costa Rica −84.00 10.46 40 Modern TRFO Swamp - - - Digi Horn, S. P. Rodgers and Horn (1996)Laguna Palmita Costa Rica −84.95 10.18 60 Modern TDFO Soil - - - Digi Horn, S. P. Rodgers and Horn (1996)La Selva, Heredia Costa Rica −84.00 10.43 80 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)La Pacifica,Guanacaste Costa Rica −85.11 10.45 110 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)Cataracta,Carara 1 Costa Rica −84.63 9.83 270 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)Cataracta,Carara 2 Costa Rica −84.63 9.85 270 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)Santa Rosa3 Costa Rica −85.62 10.86 280 Modern TSFO Soil - - - Digi Horn, S. P. Rodgers and Horn (1996)Santa Rosa4 Costa Rica −85.62 10.86 280 Modern TSFO Soil - - - Digi Horn, S. P. Rodgers and Horn (1996)Escondido Costa Rica −85.61 10.87 280 Modern TSFO Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Cafetal,Guanacaste Costa Rica −85.65 10.85 300 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)LagunaRıo Cuarto Costa Rica −84.18 10.34 380 Modern WTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Laguna Bonilla Costa Rica −83.61 9.99 380 Modern TSFO Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Laguna La Palma Costa Rica −84.73 10.49 570 Modern WTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Laguna Cedeno Costa Rica −84.71 10.49 610 Modern WTRF Lake - - - Digi Horn, S. P Rodgers and Horn (1996)Brauillo Carillo,Heredia Costa Rica −83.94 10.3 630 Modern WTRF Pollster - - - Raw Bush, M. Bush (1995), Bush and Rivera (1998)Laguna Gonzalez Costa Rica −84.45 10.25 710 Modern WTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Laguna Congo Costa Rica −84.29 10.27 740 Modern WTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Bosque Alegre Costa Rica −84.21 10.21 740 Modern WTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Laguna Hule Costa Rica −84.19 10.27 740 Modern WTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)LagunaMarıa Aguilar Costa Rica −84.18 10.27 770 Modern WTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Volcan Cacao Costa Rica −85.47 10.92 1000 Modern WTRF Soil - - - Digi Horn, S. P. Rodgers and Horn (1996)Monteverde,Heredia Costa Rica −84.8 10.3 1500 Modern WTRF Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)La Chonta Costa Rica −82.00 8.00 2310 0–80 000 CTRF Lake 3 2C 7D Digi Islebe, G. Hooghiemstra et al. (1992),

Islebe and Hooghiemstra (1997),Islebe et al. (1995a, b)

Talamancas Costa Rica −83.72 9.5 2500 Modern CTRF Pollster - - - Raw Bush, M. Bush (1995),Bush and Rivera (1998)

Volcan Poas Costa Rica −84.19 10.3 2580 Modern CTRF Pollster - - - Raw Bush, M. Bush (1995),Bush and Rivera (1998)

Laguna Botos Costa Rica −84.18 10.18 2600 Modern CTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Tres de Junio Costa Rica −83.87 9.62 2670 Modern CTRF Bog - - - Digi Horn, S. P. Rodgers and Horn (1996)Bog 68 Costa Rica −83.85 9.64 2670 Modern CTRF Bog - - - Digi Horn, S. P. Rodgers and Horn (1996)Bog 70 Costa Rica −83.85 9.61 2670 Modern CTRF Bog - - - Digi Horn, S. P. Rodgers and Horn (1996)Laguna Barva Costa Rica −84.11 10.14 2840 Modern CTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Quebrador Costa Rica −83.84 9.74 3040 Modern CTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Asuncion Costa Rica −83.75 9.64 3340 Modern CTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)Lago delas Morrenas Costa Rica −83.49 9.50 3480 0–10 000 CTRF Lake 6 2C - Digi Horn, S. P. Horn (1993)Lago Chirripo Costa Rica −83.48 9.48 3520 Modern CTRF Lake - - - Digi Horn, S. P. Rodgers and Horn (1996)

436

Table 3. Continued.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

Limoncocha Ecuador −76.66 −0.38 230 0–1300 TRFO Lake 2 - - Digi Colinvaux, P. Colinvaux et al. (1988)Anangucocha Ecuador −77.03 −0.53 280 Modern TRFO Lake - - - Digi Bush, M. Bush et al. (1990)Cuyabeno Ecuador −77.01 0 280 Modern TRFO Lake - - - Digi Bush, M. Bush et al. (1990)Lago Agrio Ecuador −77.03 0.03 330 Modern TRFO Lake - - - Digi Bush, M. Bush et al. (1990),

Colinvaux et al. (1988)Santa Cecilia Ecuador −77.03 0.04 330 Modern TRFO Lake - - - Digi Bush, M. Bush et al. (1990)Lake SantaCecilia Ecuador −77.02 0.06 330 0–1000 TRFO Lake 2 - - Digi Colinvaux, P. Colinvaux et al. (1988),

Bush et al. (1990)Ayauch Ecuador −78.13 −2.09 550 0–7500 WTRF Lake 4 2C - Digi Bush, M. Bush et al. (1990),

Bush and Colinvaux (1988),Colinvaux et al. (1988)

Kumpack Ecuador −78.51 −1.53 700 Modern WTRF Lake - - - Digi Bush, M. Bush et al. (1990)Puyo Bog Ecuador −79.06 −1.43 953 Modern WTFO Lake - - - Digi Bush, M. Bush et al. (1990)Lake Surucucho Ecuador −78.95 −3.75 970 0–12 000 WTRF Lake 9 4C - Digi Colinvaux, P. Colinvaux et al. (1997)Mera Ecuador −76.92 0.11 1100 Modern WTRF Lake - - - Digi Bush, M. Bush et al. (1990)Indanza Ecuador −78.83 −1.53 2100 Modern CTRF Lake - - - Digi Bush, M. Bush et al. (1990)Yaguara cocha Ecuador −79.03 0.13 2210 Modern CTRF Lake - - - Raw Bush, M. Bush et al. (1990)Rum Tum Ecuador −79.03 −1.13 2392 Modern CTRF Lake - - - Digi Bush, M. Bush et al. (1990)Yambo Ecuador −79.03 −1.03 2600 Modern CTRF Lake - - - Digi Bush, M. Bush et al. (1990)Cunro Ecuador −79.03 0.08 2800 Modern CTRF Lake - - - Digi Bush, M. Bush et al. (1990)Llaviucu Ecuador −79.43 −1.83 3120 Modern CTRF Lake - - - Digi Bush, M. Bush et al. (1990)San Marcos Ecuador −79.03 0.03 3400 Modern CGSH Mire - - - Digi Bush, M. Bush et al. (1990)Cayambe Ecuador −78.03 −0.03 4350 0–5200 CGSH Mire 6 4D - Raw Graf, K. Graf, 1989 (1992)Lago Quexil Guatemala −89.88 16.92 110 0–27 500 TSFO Lake 4 7D - Raw Leyden, B. W. Leyden (1984),

Leyden et al. (1993, 1994)Lake Peten-Itza Guatemala −90.00 17.25 200 0–9000 TDFO Lake 7 2C - Digi Islebe, G. Islebe et al. (1996)Sierra de Cuchu-matanes 5 Guatemala −91.00 15.75 2800 Modern WAMF Pollster - - - Digi Islebe, G. Islebe and Hooghiemstra (1995)Paramo deMiranda Venezuela −70.85 8.91 3290 310–11 470 CGSH Mire 3 4C - Raw Salgado-Labouriau, M. L. Salgado-

-Labouriau, M. L. (1988, 1991)ValleLaguna Negra Venezuela −70.76 8.79 3450 0–3350 CGSH Lake 1 - - Raw Graf, K. Rull et al. (1987)ParamoPiedras Blancas Venezuela −70.83 9.16 4080 0–1340 CGSH Mire 2 - - Raw Salgado-Labouriau, M. L. Rull et al. (1987)Sierra deCuchumatanes 4 Guatemala −91.25 15.75 3000 Modern CTRF Pollster - - - Digi Islebe, G. Islebe and

Hooghiemstra (1995)Sierra deCuchumatanes 3 Guatemala −91.5 15.75 3400 Modern CTRF Pollster - - - Digi Islebe, G. Islebe and

Hooghiemstra (1995)Sierra deCuchumatanes 2 Guatemala −91.75 15.75 3600 Modern WAMF Pollster - - - Digi Islebe, G. Islebe and

Hooghiemstra (1995)Sierra deCuchumatanes 1 Guatemala −92.00 15.75 4200 Modern CGSH Pollster - - - Digi Islebe, G. Islebe and

Hooghiemstra (1995)San JoseChulchaca Mexico −90.13 20.86 1 0–7300 TDFO Lake 8 2C - Raw Leyden, B. W. Leyden et al. (1995)Lake Coba Mexico −87.55 20.86 100 5880–19 230 WAMF Playa 8 7D 2C Raw Leyden, B. W. Leyden et al. (1998)Lago Catemaco Mexico −95.00 18.66 340 0–2230 TRFO Lake 5 2C - Raw Byrne, A. R. Byrne and Horn (1989)LakePatzcuaro Mexico −101.58 19.58 2044 20–44 100 WAMF Lake 24 2C 6C Raw Watts, W. A. Saporito (1975),

Watts and Bradbuy (1982)

437

Table 3. Continued.

Site Country Long. Latitude Alt. Age Present Sample RC DC at DC at Data Analyst Siterange biome type 6000 18 000 type publications

yr BP yr BP

Chalco Lake Mexico −99.00 19.50 2240 8000–27 500 WAMF Lake 8 3C 1C Raw Lozano-Garcia, M. S. Lozano-Garcia andOrtega-Guerrero (1994),Lozano-Garcia et al. (1993),Ortega-Guerrero (1992)

Lake Texcoco Mexico −99.12 19.44 2330 0–35 000 WAMF Lake 7 3C 4C Digi Lozano-Garcıa, S. Lozano-Garcıa andOrtega-Guerrero (in press )

Quila Mexico −99.20 19.30 2800 0–10 000 WAMF Lake 4 3C - Raw Almeida, L. Almeida (1997)Zempoala Mexico −99.30 19.20 3100 0–4600 WAMF Lake 5 - - Raw Almeida, L. Almeida (1997)Soberania Panama −79.66 9.13 20 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)Ocelot Pond Panama −79.59 9.12 20 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)Pipeline Rd Panama −79.66 9.33 40 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)Barro Colorado Island Panama −79.75 9.35 50 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)Panama Panama −80.87 9.00 100 0–7200 TSRO Lake 8 1C - Raw Leyden, B. W. Leyden et al. (1995)El Valle Panama −79.78 8.43 500 0–20 000 TSFO Lake 5 - 2C Digi Bush, M. Bush (1995),

Bush and Rivera (1998),Piperno et al. (1991a, b)

Cana Swamp Panama −77.59 7.74 500 0–4600 TRFO Swamp 5 - - Digi Bush, M. Bush and Colinvaux (1994)WodehouseSwamp Panama −77.58 7.75 500 0–4200 TRFO Swamp 1 - - Digi Bush, M. Bush and Colinvaux (1994)La Yeguada, Panama −80.78 8.43 650 0–14 000 TSFO Lake 11 1C - Digi Bush, M. Bush (1995),

Bush et al. (1992),Bush and Rivera (1998),Piperno et al. (1991a, b)

Cerro Campana Panama −79.93 8.63 800 Modern WTFO Pollster - - - Raw Bush, M. Bush (1995),Bush and Rivera (1998)

Cana, Darien Panama −77.58 7.68 1000 Modern TSFO Pollster - - - Raw Bush, M. Bush (1995),Bush and Rivera (1998)

Quebrada Nelson Panama −82.31 8.66 1130 Modern WTRF Pollster - - - Raw Bush, M. Bush (1995),Bush and Rivera (1998)

Horsefly Ridge Panama −82.24 8.83 1150 Modern WTRF Pollster - - - Raw Bush, M. Bush (1995),Bush and Rivera (1998)

Laguna Volcan Panama −82.75 8.75 1500 0–2860 WTRF Lake 4 - - Digi Behling, H. Behling (2000)Finca Lerida Panama −82.45 8.87 1630 Modern WTRF Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)Volcan Irazu Panama −82.52 8.88 2300 Modern CTRF Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)Volcan Baru Panama −82.52 8.85 2600 Modern CTRF Pollster - - - Raw Bush, M. Bush (1995),

Bush and Rivera (1998)Laguna Paca Peru −75.50 −11.71 3600 0–5410 CGSH Lake 1 4D - Raw Hansen, B. C. S. Hansen and Rodbell (1995)Laguna Junin 2 Peru −76.18 −11.00 4100 0–43 000 CGSH Lake 11 6C 1C Raw Hansen, B. C. S. Hansen and Rodbell (1995)Laguna Tuctua Peru −75.00 −11.66 4250 0–11 390 CGSH Lake 4 2C - Raw Hansen, B. C. S. Hansen et al. (1994)Laguna Milloc Peru −76.35 −11.56 4325 280–11 050 CGSH Lake 1 3C - Raw Graf, K. Graf (1992)LagunaPomacocha Peru −75.50 −11.75 4450 4100–10 220 CGSH Lake 4 1C - Raw Hansen, B. C. S. Hansen et al. (1994)Laguna Jeronimo Peru −75.21 −11.78 4450 0–11 260 CGSH Lake 4 4C - Raw Hansen, B. C. S. Hansen et al. (1994)LagunaHuatacocha Peru −76.55 −10.76 4500 0–10 620 CGSH Lake 5 2C - Raw Hansen, B. C. S. Hansen and Rodbell (1995)Lake Valencia Venezuela −67.75 10.32 403 400–13 000 STEP Lake 28 1C - Raw Leyden, B. W. Bradbury et al. (1981),Valle Leyden (1985)Laguna Victoria Venezuela −70.79 8.80 3250 0–12 210 CGSH Lake 4 4C - Raw Graf, K. Rull et al. (1987)

438

Table 4. Assignment of Latin American plant functional types to Biomes.

Codes Plant functional types

TRFO man, tx, Te1, Te2, tfTSFO tx, Tr1, Tr2, Te2, tfTDFO Tr2, tf, txts, dfWTRF tx, Tr1, Te1, wtc, ctc2, tef, wteWEFO tx, Tr2, wtc, ctc2, ec, tef, wteCTRF tx, h, ctc1, tef, wte, wte1WAMF Tr2, wtc, tef, wte, tsCOMI ctc1, tef, wte1, wte4, ts1STEP sfDESE ds, dfCGSH af, aa, wte4, hCGSS af, aa, cp

439

Table 5. Latin American pollen taxa used in the biomisation analysis and their assignment to the PFTs.

PFT codes Pollen taxa

g Poaceae

man Acrostichum-type, Avicennia, Laguncularia, Rhizophoratx Alsophila, Alstroemeria, Cnemidaria, Cyathea, Dicksonia, NepheleaTr1 Alibertia, Anacardiaceae, Andira-type, Astronium, Bauhinia, Bombacaceae, Bougainvillea,

Caesalpineae, Casearia-type, Clematis, Coccoloba, Copaifera, Didymopanax, Eugenia, Euterpe,Goupia-type, Guapira, Heliocarpus, Hura, Hieronima, Hippocrateaceae, Ipomoea, Laplacea,Lecythidaceae, Leguminoseae, Loranthaceae, Macharium, Macrolobium, Malpighiaceae, Malvaceae,Maytenus, Ocotea-type, Pera, Phyllostylon, Piper, Pisonia, Psychotria, Rubiaceae, Rutaceae, Salix,Sapium, Simira, Siparuna, Spirotheca, Spondias, Symplocos, Tecoma, Trema, Xylosma, Zanthoxylum

Tr2 Acacia, Alchornea, Alibertia, Andira-type, Anacardiaceae, Apocynaceae, Astronium, Banisteriopsis,Bauhinia, Bombacaceae, Bougainvillea, Brosimum, Brunellia, Bulnesia, Bumelia-type, Bursera, Byrsonima,Caesalpineae, Celastraceae, Chrysophyllum, Clematis, Combretaceae, Copaifera, Cordia, Coriaria, Cuphea,Curatella, Didymopanax, Elaeocarpaceae, Eryngium, Erythrina, Euterpe, Gallesia, Guapira, Hieronima,Hura, Hymenophylleace, Ipomoea, Loranthaceae, Malvaceae, Maytenus, Melastomataceae, Meliaceae,Mimosa, Passiflora, Pera, Phyllostylon, Piper, Pisonia, Protium, Pseudobombax, Rhamnaceae, Rutaceae,Salix, Sapium, Schinus, Simira, Siparuna, Spirotheca, Spondias,Styrax, Symplocos, Tiliaceae,Trema, Xylosma, Zanthoxylum

Te1 Abutilon, Actinostemon concolor, Alchornea, Amanoa, Apeiba, Apocynaceae, Araliaceae, Arecaceae,Arrabidaea, Aspidosperma, Astrocarium, Begoniaceae, Bignoniaceae, Bombacaceae, Bonamia,Brosimum, Brunellia, Brownea, Calliandra, Campomanesia, Cardiospermum, Castilla, Cecropia, Cedrela,Celtis, Copaifera, Coprosma, Cucurbitaceae, Cunoniaceae, Dalbergia, Dioclea, Doliocarpus, Elaeagia,Euterpe, Ficus, Flacourtiaceae, Forsterania, Geonoma, Guapira, Guazuma, Guarea, Hura, Ilex, Inga,Iriartea, Iva xanthifolia-type, Lecythidaceae, Leguminoseae, Licania, Mabea, Macharium, Macrolobium,Macrocarpea, Malpighiaceae, Mauritia, Marcgraviaceae, Maripa, Marattia, Matayba, Melastomatacae,Meliaceae, Miconia, Moraceae, Myrsine, Myrtaceae, Mauritiella, Nyctaginaceae, Ochnaceae,Ocotea-type, Oenocarpus, Oreopanax, Palmae, Panopsis, Parahancornia, Passiflora, Plenckia, Protium,Pseudopanax laetevirens, Rauvolfia, Rhipsalis, Rubiaceae, Sapotaceae, Scheelea,Scleronema, Socratea,Sloanea, Solanaceae, Sophora, Strutanthus, Symphonia, Swartzia, Taperira, Ternstroemia cf.T. brasiliensis, Tetrochiduim, Tetraploa aristata, Thymelaeaceae, Tiliaceae, Trichilia, Trigonia,Vismia, Warsewiczia

440

Table 5. Continued.

PFT codes Pollen taxa

Te2 Abutilon, Acacia, Aegiphila, Alibertia, Apeiba, Apocynaceae, Aspidosperma, Boraginaceae,Bougainvillea, Brosimum, Brunellia, Calliandra, Cecropia, Cedrela, Celtis, Combretaceae, Croton,Cucurbitaceae, Dalbergia,Didymopanax, Dioclea, Forsterania, Hippocrateaceae, Humiria, Humulus, Ilex,Iriartea, Leguminoseae, Macrolobium, Mauritia, Melastomataceae, Miconia, Moraceae, Mysine, Myrtaceae,Ochnaceae, Ocotea-type, Palmae, Panopsis, Passiflora, Plenckia, Pseudopanax, Psychotria, Sapotaceae,Scleronema, Serjania, Sophora, Strutanthus, Swartzia, Taperira, Tiliaceae, Vismia, Warsewiczia

wtc Abies, Araucaria, Juniperus, Pinusctc2 Abies, Araucaria, Araucaria augustifolia, Cupressaceae, Dacrydium, Juniperus, Pilgerodendron,

Podocarpus, Prumnopitys andina, Saxegothaea conspicuactc1 Austrocedrus chilensis, Cupressaceae, Dacrydium, Fitzroya cupressoides-type, Pilgerodendron,

Pinus, Podocarpus, Prumnopitys andina, Saxegothaea conspicua, Taxodiumtxts Acacia, Aeschynomene, Agave, Anthurium, Aphelandra, Arrabidaea, Atamisquea, Ayenia, Bursera,

Byrsonima, Byttneria, Cabomba, Cactaceae, Caryocar, Cayaponia, Cercidium, Chomelia, Chrysophyllum,Chuquiragua, Cissus, Clusia, Combretaceae, Convolvulaceae, Cordia, Cuphea, Curatella, Dodonaea,Echinodorus, Eichhornia, Evolvulus, Hippocrateaceae, Humulus, Hyptis, Ipomoea, Larrea, Lithraea,Malpighiaceae, Manihot, Maprounea, Menispermaceae, Metopium, Miconia, Mimosa cf. M. taimbensis,Palicourea, Pepermmia, Phaseolus, Phyllanthus, Polygala, Polylepis-Acaena, Pouteria,Portulaccaceae undiff., Prosopis, Rhamnaceae, Sapium, Schefflera, Schinus, Sebastiana, Serjania,Solanaceae, Sloanea, Stryphnodendron, Tecoma, Trixis, Zornia

h Arenaria, Aristotelia, Asteraceae, Berberidaceae, Berberis, Empetrum, Ericaceae, Sisyrinchium-typeEricaceae, Sisyrinchium-type

ds Agave, Atamisquea, Cactaceae, Ephedra, Monttea aphylladf Alternanthera, Ephedra, Monttea aphylla, Xyristf Acalypha, Acanthaceae, Alcemilla, Alismataceae, Alsophila, Anemia, Antheroceros, Armeria, Artemisia,

Assulina, Asteraceae, Astelia, Begonia, Bernardia, Brassicaceae, Bravaisia, Bromeliaceae, Calyceraceae,Caperonia, Caryophyllaceae, Cassia, Cichoriaceae, Cirsium, Cruciferae, Eriogonum, Eriocaulaceae,Euphorbia, Euphorbiaceae, Fabaceae, Geraniaceae, Gomphorena, Gunnera, Hebenaria, Iresine, Justicia,Lamiaceae, Laportea, Liquidambar, Liliaceae, Lobelia, Menispermaceae, Muehlenbeckia,Nertea, Onagraceae, Orchidaceae, Pilea, Polygala, Rhaphithamnus, Rhus, Rubiaceae, Smilax, Triumfetta,Umbelliferae, Urticaceae, Verbena, Verbenaceae, Vernonia, Viburnum, Vitis

tef Acalypha, Acanthaceae, Apiaceae, Apium, Artemisia, Astelia, Azara, Borreria, Brassicaceae, Bravaisia,Bromeliaceae, Cassia, Cichoriaceae, Eriocaulaceae, Eriogonum, Euphorbia, Euphorbiaceae, Fabaceae,Genipa, Gordonia, Gunnera, Hippeastrum, Hydrocotyle, Iridaceae, Jungia, Justicia, Lachemella,

441

Table 5. Continued.

PFT codes Pollen taxa

Lamanonia, Lamiaceae, Laportea, Liliaceae, Lupinus, Malvaceae, Marcgraviaceae, Moritzia-type, Mutisia,Nertea, Onagraceae, Orchidaceae, Pamphalea, Perezia, Phaseolus, Pilea, Piscidia, Plantago,Polemoniaceae, Polygala, Portulaccaceae undiff., Pouteria, Ranunculaceae, Rubiaceae, Rumex, Satureja,Scrophulariaceae, Selaginella, Thalictrum, Triumfetta, Umbelliferae, Urticaceae, Verbenaceae,Vernonia, Vicia, Vitis, Wendtia, Xyris

sf Alternanthera, Amaranthaceae/Chenopodiaceae, Antheroceros, Armeria, Assulina, Asteraceae, Astelia,Borreria, Calyceraceae, Cardus, Caryophyllaceae, Cardus, Connarus, Cruciferae, Embothrium, Eriogonum,Eryngium, Euphorbia, Euphorbiaceae, Fabaceae, Geraniaceae, Gomphorena, Gunnera, Hebenaria,Hippeastrum, Hydrocotyle, Iridaceae, Jungia, Justicia, Liliaceae, Lamiaceae, Liquidambar, Mutisia, Nanodea,Nassauvia-type, Orchidaceae, Oxalis, Phacelia, Physalis, Plantago, Polemoniaceae, Pouteria,Ranunculaceae, Restionaceae, Rosaceae, Rubiaceae, Satureja, Scutellaria-type, Umbelliferae,Urticaceae, Vicia, Vitis, Wendtia, Xyris

af Arenaria, Astragalus, Azorella, Bartsia-type, Borreria, Bromeliaceae, Bravaisia, Campanulaceae, Cardus,Caryophyllaceae, Deschampsia antarctica, Diphasiastrum complanatum-type, Donatia, Draba, Epilobium,Eriocaulaceae, Eriocaulon, Eriogonum, Gaimardia, Gilia, Halenia, Hebenaria, Hippeastrum, Hydrocotyle,Iridaceae, Jamesonia, Labiatae, Lachemella, Lamiaceae, Liquidambar, Lupinus, Lysipomia, Montia,Moritzia-type, Muehlenbeckia, Nassauvia-type, Orchidaceae, Oxalis, Perezia, Plantago, Puya,Quinchamalium, Relbunium, Rosaceae, Rubiaceae, Rumex, Satureja, Scrophulariaceae, Scutellaria-type,Selaginella, Sisyrinchium-type, Umbelliferae, Valeriana, Viola

cp Apiaceae, Azorella, Gaimardia, Montia, Plantago, Saxifragawte Aegiphila, Allophylus, Aphelandra, Araliaceae, Azara, Baccharis, Bauhinia, Begoniaceae, Buddleja,

Bumelia-type, Clusia, Croton, Daphnopsis, Desfontainia, Elaeocarpaceae, Embothrium,Eucryphia/Caldcluvia paniculata, Euterpe, Fuchsia, Geonoma, Geraniaceae, Griselinia, Guettardia,Gunnera, Guttiferae, Hedyosmum, Heliocarpus, Humiria, Labiatae, Lomatia/Gevuina, Loranthaceae,Ludwigia, Luehea, Malpighiaceae, Matayba, Melastomatacae, Meliacea, Mimosa, Mimosa cf. M. scabrella,Mutisia, Myrica, Mysine, Nothofagus obliqua-type, Oreopanax, Palicourea, Proteaceae, Prunus,Pseudopanax laetevirens, Psychotria, Quercus, Roupala, Sambucus, Solanaceae, Stryphnodendron,Styloceras, Tepualia stipularis, Tetrochiduim, Thymelaeaceae, Trichilia, Verbenaceae, Viburnum,Warsewiczia, Weinmannia

wte1 Aegiphila, Alnus, Arecaceae, Aragoa, Arcytophyllum, Aristotelia, Azara, Banara, Banisteriopsis,Begoniaceae, Bocconia, Brunellia, Buddleja, Calandrinia, Campanulaceae, Celastraceae, Chuquiraga,Clethra, Daphnopsis, Desfontainia, Dodonaea, Drimys, Epacridaceae, Ericaceae, Fuchsia, Galium,Gaultheria ulei, Geraniaceae, Gesneriaceae, Hedyosmum, Hydrangea, Hypericum, Labiatae, Loranthaceae,Ludwigia, Maytenus, Meliaceae, Meliosma, Muehlenbeckia, Myrtaceae, Mysine, Nothofagus,

442

Table 5. Continued.

PFT codes Pollen taxa

Nothofagus antarctica-type, Ostrya-type, Proteaceae, Prunus, Pseudopanax laetevirens, Quercus, Ribes,Roupala, Sambucus, Solanaceae, Tepualia stipularis, Verbenaceae, Viburnum, Weinmannia

wte4 Abatia, Adesmia, Alcemilla, Alfaroa, Arcytophyllum, Assulina, Asteraceae, Clethra, Colignonia,Dodonaea, Ericaceae, Gaiadendron, Gaultheria ulei, Guttifera, Laurelia, Muehlenbeckia,Myrteola, Polylepis-Acaena, Ribes, Rosaceae, Tetrochiduim, Weinmannia

ts Alnus, Banksia, Carpinus, Cayaponia, Fagus, Fraxinus, Juglans, Loranthaceae,Luehea, Myrica, Populus, Styrax, Trema, Ulmaceae, Vallea.

ts1 Escallonia, Eugenia, Gordonia, Liquidambar, Luehea, Misodendrum,Myzodendron, Styrax, Ternstroemia cf. T. brasiliensis, Trema, Vallea

aa Aragoa, Arcytophyllum, Arenaria, Asteraceae, Baccharis, Cruciferae, Draba, Empetrum, Ephedra,Ericaceae, Eriogonum, Escallonia, Gentiana, Gentianaceae, Hypericum, Nassauvia-type, Puya,Rosaceae, Senecio

443

Table 6. Site locations showing biome changes from the present, 6000±500 and18 000±1000 14C yr BP.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Salina Anzotegui −63.77 −39.06 −5 STEP STEP STEPBoca de Lopez −75.36 10.85 0 TRFO TRFOCarara Biological Reserve −84.62 9.73 0 TSFO TSFOJotaordo −76.66 5.66 0 TRFO TRFOLago Crispim −48.00 −0.8 0 TRFO STEP TSFOODP site 932 −47.03 5.18 0 TRFO WTRF WTRF TSFOPatagonia −68.10 −50.00 0 CTRF CTRFReserva Volta Velha −48.38 −26.04 0 WTRF WTRFSanta Rosa 1 −85.66 10.84 0 TRFO TRFOSanta Rosa 2 −85.64 10.83 0 TSFO TSFOTortuguero −83.53 10.53 0 TSFO WTRFSan Jose Chulchaca −90.13 20.86 1 TDFO TDFO WEFOLa Mision −67.83 −53.5 5 STEP STEP STEPLagoa da Caco −43.43 −22.97 5 TDFO TDFO STEPPuerto del Hambre −70.92 −53.59 5 CTRF CTRF CTRFPoco Grande −48.86 −26.41 10 WTRF WTRFHarberton −67.16 −54.88 20 STEP STEP STEPOcelot Pond −79.59 9.12 20 TSFO WTRFOrigone −62.43 −39.08 20 STEP STEPPatagonia −72.98 −50.15 20 CTRF CTRFPatagonia −72.90 −50.20 20 CTRF CTRFPatagonia −68.30 −50.00 20 CTRF CTRFPedro Luro −62.53 −39.50 20 STEP STEPRuta 3.3 −62.59 −40.08 20 STEP STEPRuta 3.4 −62.79 −40.50 20 STEP STEPSoberania −79.66 9.13 20 TSFO WTRFCarara −84.60 9.88 35 WTRF WTRFLagoa da Curuca 2 −47.85 −0.76 35 TRFO TRFO TRFOLake Asa 3 −61.13 −62.62 35 CGSH CGSHCantarrana Swamp −84.00 10.45 36 TSFO WTRF

444

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Pipeline Rd −79.66 9.33 40 TSFO WTRFSendro Sedro Swamp −84.00 10.46 40 TSFO WTRFBarro Colorado Island −79.75 9.35 50 TSFO WTRFDichan −73.88 −49.66 50 CTRF CTRF CTRFEl Camito −76.60 2.53 50 TRFO TRFOEspuma −63.25 −40.67 50 TDFO TDFOLaguna Lincoln −74.07 −45.34 50 CTRF CTRF CTRF CGSHLaguna Lofel −74.43 −44.85 50 CTRF CTRF CTRF CGSHLaguna Six Minutes −74.33 −46.43 50 CTRF CTRF CTRF CTRFLaguna Stibnite −74.43 −46.43 50 CTRF CTRF CGSHLaguna Stibnite −74.38 −46.43 50 CTRF CTRF CTRF CGSHPatagonia −72.95 −50.15 50 CTRF CTRFPatagonia −72.90 −50.25 50 CTRF CTRFPatagonia −72.90 −50.15 50 CTRF CTRFPatagonia −72.90 −50.05 50 CTRF CTRFPatagonia −68.60 −50.05 50 CTRF CTRFPuerto Eden −74.41 −49.13 50 CTRF CTRF CTRFRio (unclear) −38.00 −5.50 50 TDFO CTRFRio Jaguaribe I −37.75 −4.43 50 TDFO STEPRio Jaguaribe II −37.76 −4.55 50 TDFO TDFOEstero Huitanque −73.82 −43.61 52 CTRF CTRF COMILaguna Palmita −84.95 10.18 60 TSFO WTRFPatagonia −72.85 −50.15 60 CTRF CTRFPatagonia −72.75 −50.15 60 CTRF CTRFCampina Grande I −35.75 −7.23 70 TSFO STEPMirim −35.30 −5.68 70 TDFO STEPPatagonia −72.80 −50.15 70 CTRF CTRFPicos −41.40 −7.06 70 TDFO WTRFRio Mirim −35.40 −5.64 70 TDFO WTRFLagoa Grande −47.45 −7.08 75 TDFO CTRFMayol −73.75 −42.64 75 CTRF CTRF CTRFPunta Arenas −70.97 −53.15 75 CTRF CTRF CTRF CGSHRano Raraku Bore 3 −109.28 −27.16 75 TDFO TDFO TDFO TDFO

445

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

La Selva, Heredia −84 10.43 80 TSFO TSFOPatagonia −72.70 −50.20 80 CTRF CTRFRio de Contas −39.00 −14.28 80 TDFO STEPRio Jequitinhonha −38.92 −15.85 80 TDFO TDFORio Protengi −35.25 −5.78 80 TDFO CTRFRio Sao Francisco −36.50 −10.26 80 TDFO STEPArroyo Sauce Chico −62.23 −38.07 85 STEP STEPGaviotas −63.65 −39.07 90 TDFO TDFOLago Bolim −35.18 −6.04 90 TDFO STEPLake Coba −87.55 20.86 100 WAMF WAMF WEFOPanama −80.87 9.00 100 TSFO WTRF TSFOPatagonia −72.55 −50.10 100 CTRF CTRFPatagonia −68.90 −50.05 100 CTRF CTRFTorres del Paine −72.66 −50.98 100 CTRF CTRF TDFOEmpalme Querandıes −60.65 −37.00 105 STEP STEP TDFOLa Pacifica, Guanacaste −85.11 10.45 110 TSFO TSFOLago Quexil −89.88 16.92 110 TSFO WAMF WEFO WAMFPuchilco −73.62 −42.63 110 CTRF CTRF CTRFRano Kao −109.43 −27.18 110 TDFO TDFORuta 250.19 −65.58 −39.54 117 STEP STEPPuerto Octay PM13 −72.90 −40.93 120 CTRF CTRF CTRF COMIComprida −47.63 5.18 130 WTRF WTRF TRFOGeral −47.53 5.18 130 WTRF WTRF TRFOChepu −73.66 −42.17 140 CTRF CTRFCarajas −48.00 −5.00 150 TSFO TSFO TSFO TSFOPatagonia −72.00 −50.15 150 CTRF CTRFPatagonia −72.00 −50.05 150 CTRF CTRFMariname-II −72.03 −0.66 160 TRFO TRFO WTRFMonica-1 −72.50 −0.60 160 TRFO TRFO TRFOCarimagua −74.14 4.04 180 TDFO TDFO CGSSPatagonia −71.95 −50.10 180 CTRF CTRFPatagonia −71.70 −50.15 180 CTRF CTRFPatagonia −71.50 −50.15 180 CTRF CTRFPatagonia −71.10 −50.15 180 CTRF CTRF

446

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Patagonia −69.90 −50.15 180 CTRF CTRFPatagonia −69.30 −50.15 180 CTRF CTRFSardinas −69.45 4.95 180 TDFO TDFO TDFOEl Pinal −70.40 4.09 185 TDFO TDFO TDFO TDFOPatagonia −71.30 −50.15 190 CTRF CTRFPatagonia −70.90 −50.15 190 CTRF CTRFPatagonia −70.20 −50.15 190 CTRF CTRFPatagonia −69.60 −50.15 190 CTRF CTRFPatagonia −69.00 −50.10 190 CTRF CTRFAguads Emendadas −47.58 −15.56 200 TDFO CTRF TSFO CGSHAtlantic −48.35 −25.95 200 WTRF WTRFCerro La China −58.64 −37.84 200 STEP TDFO TDFOCrominia −49.45 −17.28 200 TDFO TDFO TSFO TDFOLake Peten-Itza −90.00 17.25 200 TDFO TDFO WEFOMoreno Glacier Bog −73.00 −50.46 200 CTRF CTRF COMIPatagonia −70.50 −50.15 200 CTRF CTRFPiusbi −77.89 1.66 200 TRFO TRFO TRFOLaguna Angel −70.54 4.45 205 TDFO TSFO TDFOLagoBsAs −71.45 −46.44 230 CTRF WAMFLimoncocha −76.66 −0.38 230 TRFO CTRFLago Agua Sucia −73.54 3.46 260 TDFO TDFO STEPCataracta, Carara 1 −84.63 9.83 270 TSFO WTRFCataracta, Carara 2 −84.63 9.85 270 TSFO WTRFAnangucocha −77.03 −0.53 280 TRFO WTRFCuyabeno −77.01 0.08 280 TRFO WTRFEscondido −85.61 10.87 280 TSFO WTRFSanta Rosa 3 −85.62 10.86 280 TSFO TSFOSanta Rosa 4 −85.62 10.86 280 TSFO WTRFCafetal, Guanacaste −85.65 10.85 300 TSFO WTRFLa Pata −66.66 0.25 300 TRFO TSFO WTRF WTRFLagoa das Patas −66.68 0.26 300 TRFO WTRF TSFO WTRFLoma Linda −73.35 3.22 310 TDFO TDFO TDFOLa Esperanza −72.83 −46.63 330 CTRF CTRF

447

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Lago Agrio −77.03 0.03 330 TRFO WTRFLake Agrio −76.92 0.11 330 TRFO TRFOLake Santa Cecilia −77.02 0.06 330 TRFO TRFOSanta Cecilia −77.03 0.04 330 TRFO WTRFLago Catemaco −95.00 18.66 340 TRFO WAMFCuiaba −55.86 −15.35 350 TDFO TDFOLaguna Bonilla −83.61 9.99 380 TSFO WTRFLaguna Rıo Cuarto −84.18 10.34 380 WTRF WTRFLago do Pires −42.21 −17.95 390 TSFO TSFO TSFORio Sao Francisco −43.00 −10.46 400 TDFO TDFO TSFOLake Valencia −67.75 10.32 403 STEP STEP TDFORano Aroui −109.40 −27.08 425 TDFO TDFO CGSH WTRFSaquinho −43.23 −10.44 480 TSFO TSFO WEFOCana Swamp −77.59 7.74 500 TRFO TRFOCaunahue −72.00 −40.00 500 CTRF CTRFEl Valle −79.78 8.43 500 TSFO TSFO TDFOWodehouse Swamp −77.58 7.75 500 TRFO TRFOAssis −50.50 −22.68 540 TSFO TSFOAyauch −78.13 −2.09 550 WTRF WTRF TSFOBauru −49.07 −22.32 570 TSFO TSFOLaguna La Palma −84.73 10.49 570 WTRF WTRFLaguna Cedeno −84.71 10.49 610 WTRF WTRFBrauillo Carillo, Heredia −83.94 10.3 630 WTRF WTRFLagoa Santa −47.45 −22.36 630 TDFO TDFOPico Salam −67.43 −45.42 637 STEP STEPLa Yeguada −80.78 8.43 650 TSFO TSFO TSFOSan Pedro −73.95 −42.25 650 CTRF CTRFBotucatu −48.00 −23.00 700 WTRF WTRFBrotas −48.08 −22.29 700 WTRF WTRFCurcuab −48.00 −23.00 700 WTRF WTRFKumpack −78.51 −1.53 700 WTRF WTRFLaguna Gonzalez −84.45 10.25 710 WTRF WTRFBosque Alegre −84.21 10.21 740 WTRF WTRF

448

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Laguna Congo −84.29 10.27 740 WTRF WTRFLaguna Hule −84.19 10.27 740 WTRF WTRFLagoa Grande −47.45 −7.08 75 TDFO CTRFKatira −63.00 −9.00 750 TDFO TDFO TDFO TDFOLaguna Bella Vista −61.56 −13.58 750 TSFO TSFO TSFO TDFOLaguna Chaplin −61.05 −14.50 750 TSFO TSFO TDFO TSFOLaguna Marıa Aguilar −84.18 10.27 770 WTRF WTRFAlercesNor −71.60 −42.56 800 CTRF CGSHCerro Campana −79.93 8.63 800 WTRF WTRFMallin Book −71.58 −41.33 800 CTRF COMI CTRFPrimavera −71.18 −40.66 800 CTRF CGSH TDFORio da Curua −48.83 −23.83 800 WTRF TRFOComallo −70.21 −41.01 815 CGSH STEPColombo −49.23 −25.33 920 TSFO TSFOPuyo Bog −79.06 −1.43 953 WTRF WTRFEncantado −71.13 −40.66 960 CTRF COMILake Surucucho −78.95 −3.75 970 WTRF CTRF WTRFMeseta Latorre 1 −72.05 −51.52 980 CGSH COMI CTRFCana, Darien −77.58 7.68 1000 TSFO TSFOMeseta Latorre 2 −72.03 −51.44 1000 CGSH COMI CGSHVolcan Cacao −85.47 10.92 1000 WTRF WTRFBrasilia 1 −47.66 −15.59 1030 TDFO CTRFCueva Haichol −70.66 −38.58 1050 STEP STEP STEPSalitre −46.78 −19.00 1050 WTRF WTRF TDFO CGSHAustroEsqu −71.47 −42.66 1100 CTRF CTRFMera −76.92 0.11 1100 WTRF WTRFQuebrada Nelson −82.31 8.66 1130 WTRF WTRFHorsefly Ridge −82.24 8.83 1150 WTRF WTRFSerra da Boa Vista −49.15 −27.70 1160 WTRF WTRF CTRFSerra Campos Gerais −50.21 −24.66 1200 WTRF WAMF TDFOPitalito −76.50 1.75 1300 WEFO WEFO TSFOSerra do Rio Rastro −49.55 −28.55 1420 WTRF WTRF CGSHVaca Lauquen −71.08 −36.83 1450 CTRF COMI CGSH

449

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Vaca Lauquen −71.08 −36.83 1450 CTRF COMI CGSHLaguna Volcan −82.75 8.75 1500 WTRF WAMFMonteverde, Heredia −84.8 10.3 1500 WTRF WTRFFinca Lerida −82.45 8.87 1630 WTRF WAMFPiagua −76.50 2.30 1700 WEFO WEFO WEFOPantano de Genagra −76.50 2.50 1750 WEFO TDFO WEFORio Timbio −76.50 2.50 1750 WEFO WEFO WEFOMorro da Igreja −49.86 −28.18 1800 WTRF WTRF WTRFLibano −75.50 4.50 1820 WEFO WEFO COMIMorro de Itapeva −45.63 −22.78 1850 WTRF WEFO TDFO CGSHVeranada Pelan −70.38 −36.88 1860 CGSH CGSH TDFOde Pedro Palo III −74.41 4.50 2000 COMI WTRF WTRFHerrera −73.91 5.00 2000 COMI CGSH COMI CTRFSalina 2 −69.33 −32.25 2000 CGSH STEP STEPUbaque −73.55 4.33 2000 WEFO WEFOLake Patzcuaro −101.58 19.58 2044 WAMF WAMF WAMF WAMFIndanza −78.83 −1.53 2100 CTRF WTRFYaguara cocha −79.03 0.13 2210 CTRF WTRFChalco Lake −99.00 19.50 2240 WAMF WAMF WAMF WAMFVolcan Irazu −82.52 8.88 2300 CTRF CTRFLa Chonta −82.00 8.00 2310 CTRF CTRF COMI WAMFLake Texcoco −99.12 19.44 2330 WAMF WAMF WAMF WAMFRum Tum −79.03 −1.13 2392 CTRF WTRFTalamancas −83.72 9.5 2500 CTRF WAMFCiudad Universitaria X −74.18 −4.75 2560 COMI COMI WAMF WAMFEl Abra II −73.96 5.02 2570 COMI CTRF CTRFFuquene II −73.87 5.50 2580 COMI CGSH CTRF CTRFVolcan Poas −84.19 10.3 2580 CTRF CTRFLaguna Botos −84.18 10.18 2600 CTRF WTRFVolcan Baru −82.52 8.85 2600 CTRF CTRFYambo −79.03 −1.03 2600 CTRF WTRFBog 68 −83.85 9.64 2670 CTRF WTRFBog 70 −83.85 9.61 2670 CTRF WTRF

450

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Tres de Junio −83.87 9.62 2670 CTRF CTRFWasa Mayu −65.91 −17.54 2720 COMI CGSH STEPCunro −79.03 0.08 2800 CTRF WTRFQuila −99.20 19.30 2800 WAMF WAMF WAMFSierra de Cuchumatanes 5 −91.00 15.75 2800 WAMF WAMFVeranada Vulkanpickel −70.41 −36.68 2800 CGSH STEP TDFOLaguna Barva −84.11 10.14 2840 CTRF WTRFSierra de Cuchumatanes 4 −91.25 15.75 3000 CTRF CTRFQuebrador −83.84 9.74 3040 CTRF CTRFAlsacia −74.11 4.09 3100 COMI WAMF COMIZempoala −99.30 19.20 3100 WAMF WAMFLlaviucu −79.43 −1.83 3120 CTRF WTRFSalado −69.75 −35.33 3200 CGSH CGSHAgua Blanca −74.45 5.0 3250 COMI COMI CTRF CGSHValle Laguna Victoria −70.79 8.80 3250 CGSH CGSH CGSHParamo de Miranda −70.85 8.91 3290 CGSH CGSH CGSHCienaga del Visitador −72.83 6.13 3300 COMI COMI CGSHAsuncion −83.75 9.64 3340 CTRF CTRFSan Marcos −79.03 0.03 3400 CGSH TDFOSierra de Cuchumatanes 3 −91.5 15.75 3400 CTRF CTRFLa Guitarra −74.28 4.00 3450 COMI COMI CTRFValle Laguna Negra −70.76 8.79 3450 CGSH CGSHLago de las Morrenas −83.49 9.50 3480 CTRF WAMF CTRFCiega I −72.31 6.50 3510 COMI CTRFLago Chirripo −83.48 9.48 3520 CTRF WAMFLa Primavera −74.13 4.00 3525 CGSH CGSH COMIDe la America −74.00 4.33 3550 CGSH CGSH CTRFParamo Palacio −73.88 4.76 3550 CGSH CGSH CGSHAndabobos −74.15 4.09 3570 CGSH CGSH CGSHLaguna Paca −75.50 −11.71 3600 CGSH CTRFSierra de Cuchumatanes 2 −91.75 15.75 3600 WAMF WAMFParamo de Laguna Verde −74.00 5.25 3625 CGSH CGSH CGSH

451

Table 6. Continued.

Site Long. Latitude Alt. Present biome Modern 6000 18 000

Paramo de Pena Negra −74.09 5.09 3625 CGSH CGSH COMIEl Bosque −75.45 8.85 3650 CGSH CGSHTurbera de Calostros −73.48 4.41 3730 CGSH CGSHLake Huinamimarca −69.00 −16.50 3765 CGSH CGSH CGSH CGSSBobos −72.85 6.13 3800 CGSH CGSHEl Gobernador −75.00 3.95 3815 CTRF CTRF WTRFTumbre 2 −67.78 −23.31 3880 CGSH CGSH STEPValle de Lagunillas −72.34 6.50 3880 CGSH CGSH CTRFCerro Calvario −68.50 −16.50 3950 CGSH CGSH CGSHAguilar −65.75 −23.83 4000 CGSH CTRF STEPAmarete −68.98 −15.23 4000 CGSH CGSH CGSHGreja −73.70 4.86 4000 CGSH CGSH CGSHLa Rabona −74.25 4.05 4000 CGSH CGSH CTRFRio Kaluyo −68.13 −16.43 4070 CGSH CGSH STEPParamo Piedras Blancas −70.83 9.16 4080 CGSH CGSH CGSHCorazon Partido −74.25 4.00 4100 CGSH CGSHEl Trinagulo −74.25 4.00 4100 CGSH CGSHLaguna Junin 2 −76.18 −11.00 4100 CGSH COMI COMI CGSHSierra de Cuchumatanes 1 −92.00 15.75 4200 CGSH CTRFAguas Calientas −67.42 −23.08 4210 CGSH CGSH CGSHLaguna Tuctua −75.00 −11.66 4250 CGSH COMI WTRFSajama −68.88 −18.16 4250 CGSH CGSHLaguna Milloc −76.35 −11.56 4325 CGSH CGSH CGSHCayambe −78.03 −0.03 4350 CGSH CGSH CGSHCotapampa −69.11 −15.21 4450 CGSH CGSH CGSHLaguna Jeronimo −75.21 −11.78 4450 CGSH CGSH CGSHLaguna Pomacocha −75.50 −11.75 4450 CGSH CGSH CGSHLaguna Huatacocha −76.55 −10.76 4500 CGSH CGSH CGSHCumre Unduavi −68.03 −16.33 4620 CGSS CGSS CGSSAjata −69.20 −18.25 4700 CGSH CGSHChacaltaya 1 −68.13 −16.36 4750 CGSH CGSH CGSHMt. Blanco −67.35 −17.02 4780 CGSS CGSSKatantica −69.18 −14.8 4820 CGSH CGSH CGSH

452

47

Figure 1. Map of Latin America depicting the present-day summer and winter position of the ITCZ and the macro-

scale wind (and hence moisture) patterns over Latin America.

ITCZ position July ITCZ position January

Prevailing wind direction

110 90 70 50

20

40

20

0

Fig. 1. Map of Latin America depicting the present-day summer and winter position of the ITCZand the macroscale wind (and hence moisture) patterns over Latin America.

453

48

Figure 2. Map of the modern potential vegetation as derived from Schmithüsen, J. (1976) Hück (1960). For

example, the various divisions of seasonally dry forest such as Cerradõ, Caatinga, Campo Rupstre,

Savanna, are combined to the biome of tropical dry forest.

Fig. 2. Map of the modern potential vegetation as derived from Schmithusen (1976) andHuck (1960). For example, the various divisions of seasonally dry forest such as Cerrado,Caatinga, Campo Rupstre, Savanna, are combined to the biome of tropical dry forest.

454

49

Figure 3. Examples of biomes used in Latin America from cool grass shrubland from the paramo of Colombia (a,

b) to cool mixed forest (c, d, e, f) , tropical dry forest (g, h) with dominance of steppe (i) tropical rain

forest (j, k), dominated by mangrove (l), tropical seasonal forest (m), cool temperate forest (j, k). Plate 0

shows the multi-vegetated lays within cloud forest (cool temperate rainforest), some of these taxa such as

Rhipsalis and Bromeliacae are also found in very dry ecosystems. The bottom plate (l) shows the

importance of edpahic factors on controlling vegetation; in this case local hydrology where rill channels

allow trees to grow in areas that would be dominated by grassland.

d ba c

fe g h

i j k l

mJ

o p

n

Fig. 3. Examples of biomes used in Latin America from cool grass shrubland from the paramo of Colombia (a, b) tocool mixed forest (c, d, e, f) , tropical dry forest (g, h) with dominance of steppe (i) tropical rain forest (j, k), dominatedby mangrove (l), tropical seasonal forest (m), cool temperate forest (j, k). Plate 0 shows the multi-vegetated lays withincloud forest (cool temperate rainforest), some of these taxa such as Rhipsalis and Bromeliacae are also found in verydry ecosystems. The bottom plate (l) shows the importance of edpahic factors on controlling vegetation; in this caselocal hydrology where rill channels allow trees to grow in areas that would be dominated by grassland.

455

51

Figure 4. Cross an altitudinal cross section of the Andes showing the standard vegetation units and their

relationship to Biomes (a) and plant functional types (b).

5000

4000

3000

2000

1000

0

Te1Tr1

cp

sftf

Te2 Tr2

tx

af

tfwte

wte1

wte4

Alti

tude

(m)

ts

man

g

aa

ctc2

ecctc1

txts

5000

4000

3000

2000

1000

0TROPICAL RAIN FOREST

SAVANNA

LOWER ANDEAN FOREST

UPPER ANDEAN FOREST

SUB-PARAMO

PARAMO / SUPER PARAMO

CGSS

TRFO TSFO

CGSH

WAMF

WEFO

COMI

STEP

CEFO

TDFO

(a)

(b)

Alti

tude

( m)

5000

4000

3000

2000

1000

0

Te1Tr1

cp

sftf

Te2 Tr2

tx

af

tfwte

wte1

wte4

Alti

tude

(m)

ts

man

g

aa

ctc2

ecctc1

txts

5000

4000

3000

2000

1000

0TROPICAL RAIN FOREST

SAVANNA

LOWER ANDEAN FOREST

UPPER ANDEAN FOREST

SUB-PARAMO

PARAMO / SUPER PARAMO

CGSS

TRFO TSFO

CGSH

WAMF

WEFO

COMI

STEP

CEFO

TDFO

(a)

(b)

Alti

tude

( m)

Fig. 4. Cross an altitudinal cross section of the Andes showing the standard vegetation unitsand their relationship to Biomes (a) and plant functional types (b).

456

Figure 5. Theoretical biome scheme for Latin America laid on a grid of environmental space along the gradients of

temperature and plant available moisture. The locations of range of sites in Latin America are also shown

as red triangles.

-10

-5

0

5

10

15

20

25

30

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Alpha

MTC

O

clim space pollen sites

TRFO

WTRF

CTRF

TSFO

WEFO

WAMF

COMI

XER

O

DES

E

CGSH

STEP

TDFO

Moisture

Tem

pera

ture

-10

-5

0

5

10

15

20

25

30

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Alpha

MTC

O

clim space pollen sites

TRFO

WTRF

CTRF

TSFO

WEFO

WAMF

COMI

XER

O

DES

E

CGSH

STEP

TDFO

Moisture

Tem

pera

ture

Fig. 5. Theoretical biome scheme for Latin America laid on a grid of environmental spacealong the gradients of temperature and plant available moisture. The locations of range of sitesin Latin America are also shown as red triangles.

457

54

a b c d

e f g h

i j k l

m n o p

q r s t

a b c d

e f g h

i j k l

m n o p

q r s t

Fig. 6. Latin America plant functional types tropical rain green tree (Copernicia macroglossa) (a), tropical evergreentree (Piper nigra) (b), temperate summer-green tree (Betula spp.) (c), tropical xerophytic tree/shrub (Acacia spp.) (d),arctic shrub (Senecio spp.) (e), desert shrub (f), dry tropical raingreen tree (h), desert shrub Curretella spp. (i), tropicalforb (Viola spp.) (i), mangrove Rhizophora spp.) (j), xerophytic forb (Pepelanthus spp.) (k), tropical forb (Scadoxusmultiflorus) (l), temperate forb Umbelliferace (m, n), grass (o), aquatic (Nymphae spp.) (q), desert forb (Echinocereusspp.) (r, s) grass (t). 458

55

Figu

re 7

. Mod

ern

biom

es re

cons

truct

ed fr

om s

urfa

ce p

olle

n da

ta (c

ore

top,

trap

, sur

face

sed

imen

t) us

ed to

com

pare

agai

nst t

he p

oten

tial v

eget

atio

n (F

igur

e 2)

.

Fig. 7. Modern biomes reconstructed from surface pollen data (core top, trap, surface sedi-ment) used to compare against the potential vegetation (Fig. 2).

459

56

Figu

re 8

. Bio

me

reco

nstru

ctio

n at

600

0 ±

500

14C

yr B

P fr

om ra

dioc

arbo

n da

ted

foss

il po

llen.

Fig. 8. Biome reconstruction at 6000±500 14C yr BP from radiocarbon dated fossil pollen.

460

Fig. 9. Biome reconstruction at 18 000±1000 14C yr BP from radiocarbon dated fossil pollen.

461